Context

SAE International1 recently unveiled a new visual chart 91 that is designed to define the six levels of driving automation, from SAE Level 0 (no automation) to SAE Level 5 (full vehicle autonomy). It serves as the industry's most-cited reference for automated-vehicle (AV) capabilities.

Fully autonomous cars (Level 5 of automation according to SAE J3016), which can work everywhere in all conditions, are not yet on the roads. Nevertheless, major advances are making vehicle automation a reality. Systems exist on serial vehicles with Level 2/2+ (assisted driving) and even Level 3 (high automation, driving only upon system request) since 2021 on privately owned vehicles as well as on public transport driverless vehicles are offered to passengers and goods around the world. Recent demonstrators (automated shuttles and robotaxis) have the merit of proving the feasibility of automated driving as a solution for improving mobility, comfort, safety and energy efficiency.

Current regulation (UN 157 – adopted in June 2020 and voted by 60 countries) allows today vehicles to drive in L3 up to 60 km/h on carriageway roads. Original Equipment Manufacturers (OEMs) are pushing for the extension of this regulation up to 130 km/h including automated lane changes. To allow that (L3/L4 on the highway), many challenges are still to be taken up; technical challenges of course, but also non-technical challenges which are not the easiest to deal with (legal, liability, ethical, monopoly, acceptance, economical...) and that are not in the scope of this document even though some intersect with some technical considerations 80, 99, 123.

In this context, the official ambition of France was previously recalled by the President of France, who reaffirmed his willingness to deploy these solutions, to extend transport services based on the autonomous vehicle by 2021 whenever this is possible.

For public transportation, on-road experiments are conducted around the world in specific Operational Design Domains (ODDs) and first commercial services are being deployed. For example, in Russia, Yandex has launched the first commercial service in Europe in 2019 in the city of Innopolis and Waymo. One ride-hailing service using highly automated vehicles in the Phoenix metropolitan area (US). These systems are operating in geofenced controlled environments due to the lack of technology maturity that are able to deal with all road types (missing lines, construction areas, reckless road users behaviour like scooters, etc.).

Therefore, the development of alternative solutions at a large scale needs other scientific foundations and technological breakthroughs. Car makers, suppliers, infrastructure operators and academics across the world are working today on ways to make driving safer, more comfortable, more efficient and more inclusive through automation, and the race is on to bring the technology to the mass market.

In this context Inria and Valeo are internationally distinguished players especially thanks to their R&D activities on automated unmanned vehicles, Cybercars and more generally on the development of advanced intelligent sensors-based decision systems.

Motivation

Partners in numerous collaborative research projects and bilateral projects, Inria and Valeo have also collaborated in the supervision of doctoral and post-doctoral students. Many Inria researchers have also joined Valeo's R&D teams for several years. Finally, numerous technology transfer actions and joint patent applications have taken place. Motivated by this very strong collaboration for over 15 years, Inria and Valeo wanted to formalize this synergy by strengthening their links, both in the fields of research and technology transfer.

What could be better than to create a joint research team to share the same visions on mobility and transport automation? And what could be better than working together upstream on breakthrough research topics? This naturally resulted in the creation of a joint research team: the ASTRA team. This team brings together talents from three entities: the former RITS team at Inria (Paris), members of the DAR team at Valeo (Créteil) and members at Valeo.ai (Paris). Beyond the strategic vision assumed by the management of these three entities, the France Relance national plan was an important incentive for the creation of this unusual joint entity.

3.1 Research Axis 1: Vision and 3D Perception for Scene Understanding

Navigation for mobile robotics requires a robust understanding of the environment from 2D or 3D sensors. Recent learning-based vision algorithms are now able to operate in highly cluttered environments, and tasks which were considered challenging — such as semantic segmentation or object detection — are soon to be solved to a certain extent. Still, the classical supervision paradigm, which relies on large annotated datasets, cannot encompass in practice all outdoor conditions and scenarios. There is therefore a need both to relax the requirement of massive annotations and to extend the perception capability to situations unseen or rarely seen in the training data.

To that aim, in this research axis, we investigate several broad topics. First, we transversely investigate learning with less supervision with applications to various perception tasks. Focusing on outdoor vision, we conduct research relying on data-driven or physics-guided paradigms to hallucinate complex lighting/weather conditions and compensate for missing data in the training sets. Because mobile robots evolve in the physical world we also investigate how vision algorithms can provide in-depth 3D understanding of the scene from images and/or LiDAR scans.

To evaluate our research as well as to foster reproducibility, we rely on relevant recent public datasets (nuScenes 51, Waymo Open 142, Woodscapes 153, SemanticKITTI 42, CADCD 130, etc.) and intend to openly share our research results.

3.2 Research Axis 2: Localization & Mapping

Vehicle localization and environmental mapping are pillars of the perception task for an autonomous vehicle. While vehicle localization ensures the global positioning of the vehicle in its environment and local positioning with regard to the road and to the close road features, environment mapping contributes in building a useful internal representation that is exploited by the decision system.

Inria and Valeo teams have been working - separately and jointly - on the localization and mapping solutions for over the past 15 years. Many algorithms have been developed and showed their effectiveness in terms of accuracy, precision and safety expectations for autonomous driving. However, the integrity, safety, data size and costs are still challenging points that ASTRA wants to address while pursuing research on localization and pose registration using single/multisensor approaches.

3.3 Research Axis 3: Decision making, motion Planning & vehicle Control

Decision-making, maneuver and motion planning, and vehicle control are extremely vital components of the intelligent vehicle. These modules act as a bridge, connecting the perception subsystem of the environment and the bottom-level control subsystem in charge of the execution of the motion. We address these issues covering various strategies of designing the decision-making, trajectory planning, and tracking control, as well as shared driving of the human-automation to adapt to different levels of the automated driving system accounting with the driver profile.

The challenges related to decision making and path planning are mainly related to four distinct elements:

1. Errors and uncertainties introduced by the perception subsystems
2. Environment static and dynamic occlusions
3. Lack of understanding and prediction of other road users behaviors
4. Simultaneous consideration of several constraints related to: vehicles dynamics, energy consumption, passengers comfort, offense to driving rule...

Different approaches are investigated in the state of the art addressing one or several issues but, to our knowledge, none are capable of addressing all of them simultaneously. More specifically in most approaches decision and planning are dealt separately or in a way that favors one of them. Approaches based on Markov decision process (MPD, POMDP,...), path-speed profiles, ontologies, artificial potential fields coupled to MPC controllers are able to show interesting results in dedicated environments or in specific situations, however most of them do not tackle properly specific issues such as intention and behavior predictions, interactions or multi-criteria real time optimal maneuver decision.

While continuing the investigation of end-to-end driving approaches based (inverse-)reinforcement learning decision-making approaches, we keep on improving current path-planning methods already developed by both teams at RITS and DAR: Reachable Interaction Sets 41, Artificial Potentials Fields (coupled to MPC control) which are designed for obstacle avoidance, as well as traditional path planning methods. Optimal methods based on the convex optimization and cubic splines are investigated at DAR to design optimized and robust trajectories. More specifically, we are mainly focusing on the following three scientific topics (detailed in the next sections):

• Maneuvers and trajectories prediction of surrounding road users
• Schemes for ego-vehicle actions and maneuvers decision making and motion planning
• Motion planning and trajectories generation

3.3.1 Maneuver and trajectory prediction

To achieve a safe and comfortable driving, an autonomous driving system must have an accurate knowledge of the future motions of all other traffic agents surrounding the autonomous vehicle, such as cars, pedestrians, cyclists, etc. Motion prediction is thus a key task in autonomous vehicles. Several methods of motion prediction have been studied in the literature. Lefèvre et al 107 propose their classification in three levels with an increasing degree of abstraction: Physics-based models, Maneuver-based models and interaction-based models.

• Physics-based motion models. They consider that the motion of vehicles only depends on the laws of physics. The future motion is predicted using dynamic and kinematic models linking some control inputs car properties and external conditions. These models are limited to short term prediction and are unable to anticipate any change in the motion of the car caused by the execution of a particular maneuver.
• Maneuver-based motion models. They consider that the future motion of a vehicle also depends on the maneuver that the driver intends to perform. The future motion of a vehicle on the road network corresponds to a series of maneuvers executed independently from the other vehicles. These models are Unadaptable to different road layouts.

• Interaction-aware motion models. They take into account the inter-dependencies between vehicles’ maneuvers. These models require computing all the potential trajectories of the vehicles which is computationally expensive and no compatible with real-time risk assessment. Valeo has filed a patent to overcome this issue 149. This patented method is being developed in order to be tested in the automated driving prototypes.

  Fig. 2 shows a comparison of the different models including their challenges and the used algorithms.

Show image description

Motion prediction models comparison

Valeo has considered these categories in its development of the automated driving prototypes Cruise4U and Drive4U. The physical-based model is used in situations when their is no knowledge about the route geometry (for example in a big roundabout without lanes), the maneuver-based in highway and urban environment when the road topology is available from HD Map or valeo Drive4U Locate map.

In the few last years, machine learning based algorithms and particularly deep learning are used in order to solve the limits of the current prediction methods. Human motion trajectory prediction has been addressed in the literature 47, 137. A large amount of naturalistic road user trajectories in different contexts (highways 60, 61, 97 or urban 51, 53) needed to train and evaluate deep learning methods are now available. Our first works 12,121,11,119, taking as input the track history of a target vehicle and of its surrounding moving road users, obtained accurate prediction results of the target vehicle motion on highways and an extension 120, including the static scene structure, has been proposed for an urban context. Valeo is involved in this research area with activities in prediction of other road users and ego-vehicle trajectory. Different approaches have been implemented and tested in simulation and on test cars 50, 49.

However, work has still to be done in this domain in terms of performance, robustness and generalization before being used in real autonomous driving applications. In fact, the behavior of a human driver depends also on the contextual knowledge of the environment (speed limits, traffic density, day of the week, visibility, road equipment, driver's country, etc.) and on its goal 157. We plan to include these contextual cues in a prediction method, which should also compute multiple plausible trajectories representing the driver's diverse possible behaviors, give uncertainties estimations on the predictions, carry out multi-agents trajectory forecasts and should be usable in any environment. It will necessitate the use of a more complete dataset 156 composed of various driving scenarios collected from different countries, which may be completed by our own dataset collected with the help of Valeo if necessary. This work will be done in collaboration with Itheri Yahiaoui from Reims University and within the starting PhD thesis of Amina Ghoul funded by the SAMBA project.

3.4 Research Axis 4: Large scale modeling and deployment of mobility systems in Smart Cities

While axes 1 to 3 deal with subjects related to the on-board intelligence of an “individual” intelligent vehicle and its autonomous navigation, axis 4 intervenes when it comes to many communicating, autonomous or automated vehicles but also when it comes to the cooperation with the static environment (infrastructure). The latter may contain and integrate: roadside and monitoring sensors (Cooperative Perception Services), signaling, communication infrastructures, cloud... The research concerns in particular the deployment of equipped vehicles on a large scale in a road or urban environment.

The research objectives are twofold.

• First, the focus is on the modeling of systems comprising a large number of vehicles, often seen as random entities.

  The methodology is mainly to explore the links between large random systems and statistical physics. This approach proves very powerful, both for macroscopic (fleet management 64) and microscopic (car-level description of traffic, formation of jams 72, 139, 77, 76) analysis. The general setting is mathematical modelling of large systems (typically in the so-called thermodynamical limit), without any a priori restriction: networks, random graphs, etc. One often aims at establishing a classification based on criteria of a twofold nature: quantitative (performance, throughput, etc) and qualitative (stability, asymptotic behavior, phase transition, complexity).

• The second objective concerns the cooperation of these communicating entities in order to address the efficiency and safety of mobility. This cooperation takes several forms. Direct or indirect communications (V2X) are dedicated to maneuver coordination, taming and improving traffic efficiency (cf. section 4.4.2), platooning, safety critical distributed coordination (cf. 4.4.3)... Crowdsourcing is another aspect that could be used for traffic modeling and prediction (cf. 4.4.1), environment augmented mapping, or global vehicles localization. A Phd student will be hired this year to work on this precise subject (cf. 4.5).

Beside this core methodology, other past activities of interest include discrete event simulation 57, 102 and resource allocation for ITS 101, 84, 85.

Finally, axis 4 does not represent a structural unit like the other axes. Its objective is to deal with the problem of scaling, deployment and multi-vehicle cooperation in a global and systemic way. On the substance, methods and theories of modeling will be investigated and the design of secure telecommunication systems will be elaborated. These models and systems are intended to be implemented in more global systems and architectures. They will interact with the other axes through these architectures and will respond in a targeted way to needs; for example, whenever a need for probabilistic modeling is expressed (e.g. section 4.5).

5.1 Awards

• PaSCo 19 was nominated as Best Paper Award Candidate of CVPR 2024 (0.2% of the submissions). Authors: Anh-Quan Cao (Inria), Angela Dai (TUM), Raoul de Charette (Inria).

6.1 Open data

Several works were released as opensource work in 2024:

• MaterialPalette. A method for extraction of Materials from a Single Image. https://github.com/astra-vision/MaterialPalette
• FaMix. A Simple Recipe for Language-guided Domain Generalized Segmentation. https://github.com/astra-vision/FAMix
• PaSCo. Urban 3D Panoptic Scene Completion with Uncertainty Awareness. https://github.com/astra-vision/PaSCo
• UMBRAE. Unified Multimodal Brain Decoding. https://github.com/weihaox/UMBRAE
• LatteCLIP. Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts. https://github.com/astra-vision/LatteCLIP
• ProLIP. CLIP's Visual Embedding Projector is a Few-shot Cornucopia. https://github.com/astra-vision/ProLIP
• PODA/PIDA. Domain Adaptation with a Single Vision-Language Embedding. https://github.com/astra-vision/PODA
• MaterialTransform. Material Transforms from Disentangled NeRF Representations. https://github.com/astra-vision/BRDFTransform

7.1 3D scene reconstruction and completion

Participants: Alexandre Boulch, Anh-Quan Cao, Raoul de Charette, Matteo Marengo, Renaud Marlet, Tetiana Martyniuk, Gilles Puy.

This research direction is a long standing axis in the Astra team and before RITS. It consists of reconstructing the 3D geometry of a scene (possibly, with semantics), from a partial 3D input (eg, lidar scan) or 2D input (eg, an image). This year, most of the progress were obtained within the PhD thesis of Anh-Quan Cao. In prior works we introduced novel techniques to infer the semantically label geometry of a scene from an image 2 or from lidar scans 136, also refered as Semantic Scene Completion (SSC). In particular, we explored 3D SSC, 3D completion and 3D reconstruction all from sparse 3D measurement.

3D SSC is a semi generated tasks which applicability is limited for robotics because it does not provide information about the spatial confidence of the reconstruction. In collaboration with TUM, with Anh-Quan Cao, we extended the task of SSC and first proposed the task of panoptic semantic scene completion (PSC), which along with geometry and semantic, allows detection of instances. Our method, coined PaSCo, formulates the task as an uncertainty-aware PSC, relying on subnetworks learning different features representations. Therefore, PaSCo allows to estimate the spatial and semantic uncertainty of a scene. This has important applications for autonomous driving.

To benefit from the latest diffusion networks, which modelled denoising as Markov process, in the thesis of Tetiana Martyniuk we also explored diffusion on points and proposed a novel LiDPM method, which allowed us to identify major flaws in the literature. In a complementary direction, we also investigated how vision foundation models can be distilled for lidar processing 28.

We also investigated the hybridization of Gaussian Splatting 95 with geometric primitives for 3D reconstruction from a set of images. This work was conducted by Matteo Marengo (intern).

PaSCo 19 (webpage) was accepted at CVPR 2024, top tier conference of computer vision, and was nominated as Best Paper Award candidate (being 0.2% of the submissions). LiDPM was accepted at the 2025 Intelligent Vehicle symposium (soon online). Both works are shared opensource. Lastly, Anh-Quan Cao defended his PhD thesis in December 2024 32 revolving around 3D scene understanding.

7.2 Scene understanding with vision and language

Participants: Yasser Benigmim, Alexandre Boulch, Andrei Bursuc, Anh-Quan Cao, Raoul de Charette, Mohammad Fahes, Tuan-Hung Vu.

In the last 5 years, we have been exploring the interaction of vision and language to improve downstream tasks of visual scene understanding. This year's work was primarily focused on semantic segmentation and object classification, primarily within the thesis of Mohammad Fahes, but also with the PhD visit of Yasser Benigmim and the PhD of Anh Quan Cao. In particular, we have explored how to adapt vision language foundation model (VLMs) in a robust and parameter efficient manner.

In a first line of work, within the scope of Mohammad's thesis, we extended PODA — a prior model that demonstrated the ability to adapt to new domains by leveraging natural language 3. In our extension 33, submitted to a journal, we have shown that adaptation can be performed with any agnostic visual or language which might a prompt, a learned concept, or an image. Leveraging either of them, we learn affine transformation of the features with only a few thousands parameters, reaching extremely fast training (few seconds) for semantic segmentation. This showcases the benefit of this line of work, which already led to a few publications: 3, 22.

Since the vast majority of the literature relies on the CLIP model, we also conducted thorough experimental evaluations leading to two key observations: a) minimal adaptation of the visual-to-text projection layer can lead to a significant downstream boost, b) while the literature relies on the aggreggation of text templates, we discovered that individual templates perform differently on different semantic problem. Building on a) in Mohammad's thesis we proposed a novel approach, coined ProLIP 34, exploiting frugal learning for few-shot classification, finetuning only 5% of the parameters but leading to a large boost of performance over 11 datasets and 5 tasks. Observation b) was exploited in the work of Yasser Benigmim, which exploited the emergence of `class-experts' (individual template that outperform the aggreggation of all templates) to propose a novel training-/label-free method, named FLOSS, that outperforms the state-of-the-art. Interstingly, this plug-and-play approach allows a boost of performance on all datasets and models tested.

In a collaboration with Amazon, within Quan's thesis, we exploited the generative capability of Large Mulitmodal Models (LMMs) to annotate data unsupervisedly, which we exploited as pseudo labels for object classification 20. The resulting method, coined LatteCLIP, led to a boost of performance.

This axis of research is still on going and overall, language and other non-visual modalities, are vastly investigated in a number of our research projects. LatteCLIP was accepted at WACV 2025, and both ProLIP and FLOSS were submitted to ICCV 2025. The extension of PODA is submitted to IJCV. All of these are the top-tier venues of computer vision. All works are shared opensource.

7.3 Material edition in images

Participants: Ivan Lopes, Raoul de Charette.

In the thesis of Ivan Lopes we have explored the ability to decompose visual signals, into intrinsics maps. This is especially important to understand the nature of the materials (fabric, stone, etc.) in a scene. This problem is notoriously very complex, as it is underconstrained unless the same material is observed in multiple lighting and viewing conditions.

In a collaboration with Université Laval (Canada), we proposed a novel method to learn the transformation of Bi-directional Reflectance Distribution Functions (BRDFs, ie, implicit material representations) by leveraging a volumetrice representation of the scene (ie, NeRF). The resulting work, BRDFTransform 23 was accepted to a conference. In a prior work, MaterialPalette 24, we also exploited the capacity of diffusion models to estimate materials in images. As a follow-up, in a collaboration with Adobe (UK and Canada), we demonstrated the ability to directly edit materials in images by formulating the task as a global generation problem with a diffusion model. The results are showcasing impressive visual improvements compared to the literature. The work, named MatSwap, was recently submitted.

BRDFTransform 23 was accepted to EuroGraphics 2025, while MatSwap 36 was submitted to SIGGRAPH 2025 recently. These venues are among the best in computer graphics. All works are opensource.

7.4 Physically interpretable scene understanding

Participants: Andrei Bursuc, Raoul de Charette, Soumava Paul, Tuan-Hung Vu, Clément Weinreich.

We have explored the problem of physical plausibility and interpretability for visual scene understanding. This axis, follows major works conducted by the team exploring the use of physics model to guide or improve the training of neural networks 14, 13, 16, 7. While vision models were proven very powerful, our research in this axis aims to answer one question: Can neural network understand physics ?

In the last year, we explored two relatively small topics through internships. In the one of Clément Weinreich, we proposed a method to learn a physically explicit world model – capable of predicting the next state of the world –, by leveraging on graph neural networks. The preliminary results were very encouraging, but were not sufficient for publication. In the work of Soumava Paul, we explored the ability to build a 3D dataset annotating physics forces, point-wise, which required a major simulation labor. The current results are still preliminary but expected to be submitted a machine learning conference.

In a loosely related topic, we have explored with Weihao Xia (PhD Visit), the ability to decode brain signals to extract interpretable representation of the world. Our method, coined UMBRAE 29, is among the first method to prompt directly a model processing a brain signal (fMRI), therefore allowing visual decoding, as well as captioning, and prompting. Further, a major novelty is the ability to learn a cross-subjects representation which solve a major limitation of the literature. This collaboration with Cambridge and UCL led to a conference paper.

UMBRAE was accepted to ECCV 2024, a top-tier computer vision venue. The work of Soumava is expected to be submitted to a conference. All works are (or will be) shared opensource.

7.5 On Enhancing Intersection Applications With Misbehavior Detection and Mitigation

Participants: Jiahao Zhang, Fawzi Nashashibi.

This work has been conducted in collaboration with external partners from IRT-SystemX Institute: Ines Ben-Jemaa, Ziyi Liu and Francesca Bassi.

Collective Perception Services (CPS) enable communicating entities to share their perception data in the V2X communication network. Potential attacks on extended perception data affect the CPS and may consequently degrade the safety application that rely on collective perception data. We build in 31 an architecture that allows the integration of misbehavior detection and mitigation mechanisms with the CPS. We implement the Intersection Movement Assist (IMA) application that uses the extended perception data to calculate potential collision risks in intersection areas. We define specific safety metrics and through extensive simulations in large scale scenarios, we quantify the impact of a large number of attacks and of misbehavior detection on the safety application. Our evaluation demonstrates the ability of misbehavior detection and mitigation mechanisms to filter malicious shard perception data and consequently the benefits of using such mechanisms in improving the robustness of the safety application in complex road scenarios.

These results rely on a unified simulation framework 30 made available to the research community that enables exploration and development of misbehavior detection and mitigation solutions as integrated parts of the CPS in various scenarios. We demonstrate the effectiveness of our framework in generating performance results and provide the corresponding datasets.

7.6 PathDCM: An interpretable path-based trajectory prediction model

Participants: Amina Ghoul, Fawzi Nashashibi, Itheri Yahiaoui.

To navigate traffic safely while providing passengers with a smooth ride, autonomous vehicles must accurately predict the trajectories of surrounding agents. Predicting future trajectories is inherently uncertain and complex, as agent movements are highly non-linear over longer prediction horizons. Moreover, the distribution of possible future trajectories is multimodal—agents may have several plausible goals and different paths to reach each goal.

Despite these challenges, agent motion is not entirely unconstrained. Vehicles generally follow the direction of their lanes, obey traffic signals, and make legal turns and lane changes. Bicyclists tend to stay in bike lanes, while pedestrians usually walk along sidewalks and crosswalks. High-definition (HD) maps of traffic scenes capture these constraints, making them a critical component of autonomous driving datasets. Many studies have shown that predicting map-compliant trajectories—those that adhere to road boundaries and traffic rules—is essential for real-world autonomous driving systems.

Previous works have utilized HD maps for trajectory prediction in two main ways. First, HD maps are often used as inputs to models. Early approaches employed rasterized HD maps with CNN encoders, while more recent methods use vectorized HD maps with PointNet encoders, graph neural networks, or transformer layers. These map encodings are then passed to a multimodal prediction header that generates multiple future trajectories along with their probabilities. However, a limitation of this approach is that the prediction headers must learn a complex one-to-many mapping from the scene context to various possible trajectories, which can result in non-map-compliant predictions. To address this issue, recent research has turned to goal-based prediction models. These models associate each mode of the trajectory distribution with a 2D goal location sampled from the HD map. They predict a discrete distribution over these goals and generate trajectories conditioned on each goal. This simplifies the multimodal prediction task and makes each trajectory mode more interpretable. However, using a single 2D goal location as a conditioning factor often leads to imprecise or non-compliant trajectories that may go off-road or break traffic rules.

We introduce PathDCM, a novel approach that combines an interpretable, socially-aware framework with goal representations informed by map-aware road geometry. Unlike traditional methods that primarily condition trajectory predictions on future goals, our approach leverages the paths leading to those goals. This ensures that predictions remain physically plausible and reachable.

In addition, to achieve interpretability, our method integrates a knowledge-based discrete choice model (DCM) with a neural network. The DCM provides interpretable, rule-based patterns that explain high-level decision-making, while the neural network offers flexibility and predictive power. This hybrid approach allows us to validate predictions in safety-critical applications by ensuring that the model's decisions are both accurate and comprehensible.

PathDCM employs a three-step process: predicting goals using a hybrid approach combining knowledge-based and neural network techniques, identifying feasible paths, and generating trajectories. Our experimental evaluation on the nuScenes dataset underscores the accuracy and practical utility of our approach.

7.7 Fast maneuver recovery from aerial observation: trajectory clustering and outliers rejection

Participants: Nelson De Moura, Fernando Garrido, Augustin Gervreau, Fawzi Nashashibi.

The implementation of road user models that realistically reproduce a credible behavior in a multi-agent simulation is still an open problem. A data-driven approach consists on to deduce behaviors that may exist in real situation to obtain different types of trajectories from a large set of observations. The data, and its classification, could then be used to train models capable to extrapolate such behavior. Cars and two different types of Vulnerable Road Users (VRU) will be considered by the trajectory clustering methods proposed: pedestrians and cyclists. The results reported in 25 evaluate methods to extract well-defined trajectory classes from raw data without the use of map information while also separating “eccentric” or incomplete trajectories from the ones that are complete and representative in any scenario. Two environments will serve as test for the methods develop, three different intersections and one roundabout. The resulting clusters of trajectories can then be used for prediction or learning tasks or discarded if it is composed by outliers.

7.8 Improving behavior profile discovery for vehicles

Participants: Nelson De Moura, Fernando Garrido, Fawzi Nashashibi.

Multiple approaches have already been proposed to mimic real driver behaviors in simulation. A new one is proposed in 26, based solely on the exploration of undisturbed observation of intersections. From them, the behavior profiles for each macro-maneuver will be discovered. Using the macro-maneuvers already identified in previous works, a comparison method between trajectories with different lengths using an Extended Kalman Filter (EKF) is proposed, which combined with an Expectation-Maximization (EM) inspired method, defines the different clusters that represent the behaviors observed. This is also paired with a Kullback-Liebler divergent (KL) criteria to define when the clusters need to be split or merged. Finally, the behaviors for each macro-maneuver are determined by each cluster discovered, without using any map information about the environment and being dynamically consistent with vehicle motion. By observation, it becomes clear that the two main factors for driver's behavior are their assertiveness and interaction with other road users.

7.9 Adapting COR-MP to Various Driver Profiles

Participants: Karim Essalmi, Fernando Garrido, Fawzi Nashashibi.

As driverless cars will share the road with human drivers, it is essential to understand how humans make driving decisions to facilitate smoother collaboration and adaptation between both. It is clear that each road user behaves differently while driving: some act aggressively, while others are more conservative. To account for these varying driving behaviors, we improve our previous model, COR-MP (Conservation of Resources Model for Maneuver Planning) 21, by incorporating fuzzy logic to compute certain model parameters. In this work, we defined three distinct driver profiles: agile (behaving more aggressively), conservative (driving safely, sometimes too conservatively), and moderate (striking a good balance between the two). This profiling leads to different outcomes for the same scenario, depending on the driver profile, and results in more human-like decision-making.

7.10 Extended Horizon Planning for Tactical Decision-Making for Automated Driving

Participants: Karim Essalmi, Fernando Garrido, Fawzi Nashashibi.

Traditional decision-making algorithms are often limited by their fixed planning horizons, typically up to 6 seconds for classical approaches and 3 seconds for learning-based methods, which restrict their adaptability in particular dynamic driving scenarios. However, planning needs to be done well in advance in environments such as highways, roundabouts, and exits to ensure safe and efficient maneuvers. To address this challenge, we propose a hybrid method that integrates Monte Carlo Tree Search (MCTS) with our prior utility-based framework, COR-MP (Conservation of Resources Model for Maneuver Planning) 21. This combination enables long-term, real-time decision-making, significantly improving the ability to plan a sequence of maneuvers over extended horizons while avoiding the 'robot-frozen' phenomenon.

7.11 Communicating Autonomous Intelligent Vehicles

Participants: Gérard Le Lann.

Cyberthreats directed at radio communications cannot be ignored when addressing safety issues arising with risk-prone maneuvers. To be specific, consider unsignalized intersections (UIs) and communicating autonomous vehicles (CAVs). Most published solutions for the problem of how to achieve safe and efficient crossings in the presence of radio cyberattacks rest on assuming that destructions or corruptions of radio messages can be handled appropriately, i.e., correctly and on time.

These are fragile assumptions. Safety is inevitably compromised under cyberattacks aimed at individual messages. Safety in UI scenarios is a particular instance of the well-known global state problem in distributed systems. Safety cannot hold if CAVs in approach do not share the same global state (CAVs located on entrant road arteries and intending to cross). Building distributed global states (as they evolve over time) to be “seen” identically by all CAVs is impossible in the presence of selective destructions or corruptions of messages.

Thus, the problem: how to make use of radio communications without relying on message passing? There is a solution based on a cyber-physical construct that matches the setting (UI crossing by CAVs) for arbitrary intersections (any number of entrant road arteries, and any number of lanes in every road artery).

Another issue of sociological importance has arisen with the emergence of AI and the much-debated singularity postulate. According to supporters of the singularity concept, levels of universal cognition, consciousness, and reasoning capabilities of AI can only get higher over time. To such an extent that humans will inevitably end up being dominated (i.e., enslaved) by perverted AI able to set up offensive, potentially lethal, strategies unbeknown to humans.

One counter-argument to the singularity postulate rests on observing that lives or physical integrity of humans can hardly be threatened by software entities residing in cyberspace. The issue gets more intriguing when considering AI physical agents (AIP agents), i.e., AI agents that can act upon the physical world, such as humanoid robots or androids.

CAVs equipped with AI software are examples of AIP agents. Passengers could be targeted by perverted intelligent CAVs without passengers, insidious members of a swarm that would then face a “singularity scenario”. So far, it has been impossible to provide a description of how pernicious intelligent CAVs would communicate and agree on some destructive cyberattack, secretly, i.e., without onboard systems of honest CAVs being able to notice. Via a secret platform, using some secret language? Unclear. That line of reasoning is by no means an impossibility proof. More work is needed from scientists, who bear and share the responsibility of clarifying the issue.

7.12 Landmark localization for Autonomous Vehicles

Participants: Noël Nadal, Fawzi Nashashibi, Jean-Marc Lasgouttes.

This study introduces a new approach for real-time global positioning of vehicles, leveraging coarse landmark maps with Gaussian position uncertainty. The proposed method addresses the challenge of precise positioning in complex urban environments, where global navigation satellite system (GNSS) signals alone do not provide sufficient accuracy. Our approach is to achieve a fusion of Gaussian estimates of the vehicle's current position and orientation, based on observations of the vehicle, and information from the landmark maps. It exploits the Gaussian nature of our data to achieve robust, reliable and efficient positioning, despite the fact that our knowledge of the landmarks may be imprecise and their distribution on the map uneven. It does not rely on any particular type of sensor or vehicle. We have evaluated our method through our custom simulator and verified its effectiveness in obtaining good real-time positional accuracy of the vehicle, even on a large scale.

This work is described in a paper that will be presented at the VEHITS 2025 conference 27.

7.13 Asymptotics and Time-Scaling of Stop-and-Go Waves in Car-Following Models

Participants: Guy Fayolle, Jean-Marc Lasgouttes.

Waves, known as stop-and-go waves or phantom jams, can appear spontaneously in dense traffic. This causes a situation where drivers are faced with consecutive phases of acceleration and braking. Although waves are well understood in the setting of macroscopic models, the results for car-following models are not so numerous. Assuming string instability, G. Fayolle and J.-M. Lagouttes obtain asymptotic estimates of the speed and shape of these waves. It relies on the well-known saddle-point method in order to describe the trajectory of a vehicle caught in such a wave 35.

7.14 Thermodynamical limits for models of car-sharing systems: the Autolib' example

Participants: Guy Fayolle.

Ch. Fricker (Inria-Paris) and G. Fayolle analyze various mean-field equations obtained for models involving a large station-based car-sharing system in France called Autolib'. The focus is mainly on a version without capacity constraints, where users reserve a parking space when they take a car. The model is carried out in thermodynamical limit, that is when the number $N$ of stations and the fleet size $M_N$ tend to infinity with $U={lim}_{N\to \infty }{M}_N/N$. This limit is described by Kolmogorov's equations of a two-dimensional time-inhomogeneous Markov process depicting the numbers of reservations and cars at a station. It satisfies a non-linear differential system having a unique solution, which converges, as $t\to \infty$, exponentially fast towards an equilibrium point, which corresponds to the stationary distribution of a two-queue tandem (reservations, cars), that is always ergodic. The intensity factor of each queue has an explicit form obtained from an intrinsic mass conservation relationship. Two related models with capacity constraints are also presented: the simplest one with no reservation leads to a one-dimensional problem; the second one corresponds to our first model with finite total capacity $K$ 17.

7.15 A Markovian Analysis of IEEE 802.11 Broadcast Transmission Networks with Buffering and back-off stages

Participants: Guy Fayolle.

G. Fayolle and P. Mühlethaler (Inria-Paris) analyze the so-called back-off technique of the IEEE 802.11 protocol in broadcast mode with waiting queues. In contrast to existing models, packets arriving when a station (or node) is in back-off state are not discarded, but are stored in a buffer of infinite capacity. The key point of the analysis hinges on the assumption that the time on the channel is viewed as a random succession of transmission slots (whose duration corresponds to the length of a packet) and mini-slots during which the back-off of the station is decremented. These events occur independently, with given probabilities. The state of a node is represented by a three-dimensional Markov chain in discrete-time, formed by the back-off counter, the number of packets at the station, and the back-off stage. The stationary behaviour can be explicitly solved. In particular, stability (ergodicity) conditions are obtained and interpreted in terms of maximum throughput, see article 18.

8.1 Bilateral contracts with industry

Participants: Fawzi Nashashibi, Raoul de Charette, Jean-Marc Lasgouttes, Benazouz Bradai, Paulo Resende, Zayed Alsayed, Fernando Garrido, Axel Jeanne, Nelson de Moura, Noel Nadal, Mohammad Fahes, Karim Essalmi, Tetiana Martyniuk.

Valeo Group: As a result of a long-standing collaboration, the strategic partnership between INRA and VALEO led to the establishment of a joint project team in 2022. Since that date, several bilateral contracts were signed to conduct joint some of which are funded by Valeo.

• Several CIFRE theses have been developed throughout the year 2023 between Valeo and Inria : Mr. Karim ESSALMI joined ASTRA in February 2023 as a new PhD student working on Maneuver decision and Motion planning. Mrs Tetiana MARTYNIUK joined the team in June 2023 on a pre-thesis contract with a CIFRE that will start in 2024 and is working on conditioned generation of egocentric 3D driving scenes within the astra vision team.
• Other PhD students and post-docs are jointly funded by Valeo and Inria while Mr. Nelson de Moura is hired as a 2-years post-doc thanks to the national Plan de relance Programme.
• Valeo is currently a major financing partner of the “GAT” international Chaire/JointLab in which Inria is a partner. The other partners are: UC Berkeley, Shanghai Jiao-Tong University, EPFL, IFSTTAR, Stellantis and SAFRAN.
• Technology transfer is also a major collaboration topic between ASTRA and Valeo as well as the development of a road automated prototype.
• Finally, Inria and Valeo are partners of the French project SAMBA (Sécurité Active et MoBilités Autonomes) including SAFRAN Group, Inria Paris, TwinswHeel, Soben, Stanley Robotics and EXPLEO.

The work with Valeo Group is articulated around the collaboration of two Valeo teams:

Valeo DAR works on research and development for Advanced Driving Assistant Systems (ADAS). Starting from July 2022, Zayed Alsayed, Axel Jeanne, Fernando Garrido, and Paulo Resende, employees seconded by Valeo, joined the joint project team to work on the following scientific areas: localization and mapping (Sec. 3.2), decision making, motion planning & vehicle control (Sec. 3.3), and large-scale modeling and deployment of mobility systems in smart cities (Sec. 3.4).

Valeo.AI is the research laboratory of Valeo Group, and follows an academic research line. Valeo.AI collaborates with the vision group (Sec. 3.1). Starting from July 2022, Alexandre Boulch, Andrei Bursuc, Gilles Puy, Patrick Pérez, Renaud Marlet, Tuan-Hung Vu joined as part-time researchers in Astra, with frequent joint group readings, workshops and seminars. Subsequently to his departure from Valeo, in Dec. 2023 Patrick Pérez also left Astra. In 2024 the collaboration led to open source realizations, top-tier publications and the co-supervision of 3 internship and 2 PhDs.

9.1 International research visitors

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

10.3 Popularization

11.1 Major publications

• 1 inproceedingsZ.Zayed Alsayed, G.Guillaume Bresson, A.Anne Verroust-Blondet and F.Fawzi Nashashibi. 2D SLAM Correction Prediction in Large Scale Urban Environments.ICRA 2018 - International Conference on Robotics and Automation 2018Brisbane, AustraliaMay 2018HAL
• 2 inproceedingsA.-Q.Anh-Quan Cao and R.Raoul de Charette. MonoScene: Monocular 3D Semantic Scene Completion.Conference on Computer Vision and Pattern Recognition (CVPR)New orleans, USA, United StatesJune 2022HALback to text
• 3 inproceedingsM.Mohammad Fahes, T.-H.Tuan-Hung Vu, A.Andrei Bursuc, P.Patrick Pérez and R.Raoul de Charette. PØDA: Prompt-driven Zero-shot Domain Adaptation.International Conference on Computer Vision (ICCV)Paris, FranceOctober 2023HALback to textback to text
• 4 bookG.Guy Fayolle, R.Roudolf Iasnogorodski and V. A.Vadim A. Malyshev. Random Walks in the Quarter Plane: Algebraic Methods, Boundary Value Problems, Applications to Queueing Systems and Analytic Combinatorics.40Probability Theory and Stochastic ModellingSpringer International PublishingFebruary 2017, 255HALDOI
• 5 articleC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes, A.Alessandro Attanasi, M.Marco Pezzulla and G.Guido Gentile. Short-term Forecasting of Urban Traffic using Spatio-Temporal Markov Field.IEEE Transactions on Intelligent Transportation Systems2382022, 10858-10867HALDOIback to text
• 6 articleD.David González Bautista, J.Joshué Pérez, V.Vicente Milanés and F.Fawzi Nashashibi. A Review of Motion Planning Techniques for Automated Vehicles.IEEE Transactions on Intelligent Transportation SystemsApril 2016HALDOI
• 7 inproceedingsS. S.Shirsendu Sukanta Halder, J.-F.Jean-François Lalonde and R.Raoul de Charette. Physics-Based Rendering for Improving Robustness to Rain.ICCV 2019 - International Conference on Computer VisionSeoul, South KoreaOctober 2019HALback to text
• 8 articleM.Maximilian Jaritz, T.-H.Tuan-Hung Vu, R.Raoul de Charette, E.Emilie Wirbel and P.Patrick Pérez. Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence452March 2022, 1533-1544HALDOIback to textback to text
• 9 reportG.Gérard Le Lann. Cyberphysical Constructs and Concepts for Fully Automated Networked Vehicles.RR-9297INRIA Paris-RocquencourtOctober 2019HAL
• 10 articleI.Imane Mahtout, F.Francisco Navas, V.Vicente Milanés and F.Fawzi Nashashibi. Advances in Youla-Kucera parametrization: A Review.Annual Reviews in ControlJune 2020HALDOIback to text
• 11 articleK.Kaouther Messaoud, I.Itheri Yahiaoui, A.Anne Verroust-Blondet and F.Fawzi Nashashibi. Attention Based Vehicle Trajectory Prediction.IEEE Transactions on Intelligent Vehicles612021, 175-185HALDOIback to text
• 12 inproceedingsK.Kaouther Messaoud, I.Itheri Yahiaoui, A.Anne Verroust-Blondet and F.Fawzi Nashashibi. Non-local Social Pooling for Vehicle Trajectory Prediction.Intelligent Vehicles Symposium (IV)Paris, FranceJune 2019HALDOIback to text
• 13 inproceedingsF.Fabio Pizzati, P.Pietro Cerri and R.Raoul de Charette. CoMoGAN: continuous model-guided image-to-image translation.CVPR 2021 - IEEE Conference on Computer Vision and Pattern RecognitionIEEE Conference on Computer Vision and Pattern RecognitionOnline, FranceJune 2021HALback to textback to textback to text
• 14 inproceedingsF.Fabio Pizzati, P.Pietro Cerri and R.Raoul de Charette. Model-based occlusion disentanglement for image-to-image translation.ECCV 2020 - European Conference on Computer VisionECCV 2020Glasgow / Virtual, United KingdomAugust 2020HALback to textback to text
• 15 articleL.Luis Roldão, R.Raoul de Charette and A.Anne Verroust-Blondet. 3D Semantic Scene Completion: a Survey.International Journal of Computer Vision2021HALDOIback to textback to text
• 16 articleM.Maxime Tremblay, S. S.Shirsendu Sukanta Halder, R.Raoul de Charette and J.-F.Jean-François Lalonde. Rain Rendering for Evaluating and Improving Robustness to Bad Weather.International Journal of Computer VisionSeptember 2020HALDOIback to textback to textback to text

11.2 Publications of the year

11.3 Cited publications

• 37 inproceedingsA.Alexander Amini, W.Wilko Schwarting, A.Ava Soleimany and D.Daniela Rus. Deep evidential regression.Advances in Neural Information Processing Systems (NeurIPS)2020back to text
• 38 miscI.I. Bae, J.Jaeyoun Moon, J.J. Jhung, H.H. Suk, T.T. Kim, H.H. Park, J.J. Cha, J.J. Kim, D.D. Kim and S.Shiho Kim. Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles.2020back to text
• 39 articleA.Arjun Balakrishnan, S. R.Sergio Rodriguez Florez and R.Roger Reynaud. Integrity Monitoring of Multimodal Perception System for Vehicle Localization.Sensors2020back to text
• 40 phdthesisP.Pierre de Beaucorps. Planification de trajectoire dans un environnement peu contraint et fortement dynamique.Sorbonne Université2019back to text
• 41 inproceedingsP.Pierre de Beaucorps, A.Anne Verroust-Blondet, R.Renaud Poncelet and F.Fawzi Nashashibi. RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles.International Conference on Control, Automation, Robotics and Vision (ICARCV)2018back to text
• 42 inproceedingsJ.J. Behley, M.M. Garbade, A.A. Milioto, J.J. Quenzel, S.S. Behnke, C.C. Stachniss and J.J. Gall. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences.International Conference~on Computer Vision (ICCV)2019back to textback to text
• 43 inproceedingsV.Victor Besnier, H.Himalaya Jain, A.Andrei Bursuc, M.Matthieu Cord and P.Patrick Pérez. This dataset does not exist: training models from generated images.International Conference on Acoustics, Speech and Signal Processing (ICASSP)2020back to textback to text
• 44 inproceedingsM.Mario Bijelic, T.Tobias Gruber, F.Fahim Mannan, F.Florian Kraus, W.Werner Ritter, K.Klaus Dietmayer and F.Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather.Conference on Computer Vision and Pattern Recognition (CVPR)2020back to textback to text
• 45 articleA.Alexandre Boulch. ConvPoint: Continuous convolutions for point cloud processing.Computers & Graphics2020back to textback to text
• 46 inproceedingsA.Alexandre Boulch, G.Gilles Puy and R.Renaud Marlet. FKAConv: Feature-Kernel Alignment for Point Cloud Convolution.Asian Conference on Computer Vision (ACCV)2020back to textback to text
• 47 articleK.Kyle Brown, K. R.Katherine Rose Driggs-Campbell and M. J.Mykel J. Kochenderfer. A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior.CoRR2020back to text
• 48 articleM.Maxime Bucher, T.-H.Tuan-Hung Vu, M.Matthieu Cord and P.Patrick Pérez. BUDA: Boundless Unsupervised Domain Adaptation in Semantic Segmentation.arXiv preprint arXiv:2004.011302020back to text
• 49 inproceedingsT.Thibault Buhet, E.Emilie Wirbel, A.Andrei Bursuc and X.Xavier Perrotton. PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving.Conference on Robot Learning (CoRL)2020back to text
• 50 miscT.Thibault Buhet, E.Emilie Wirbel and X.Xavier Perrotton. Conditional Vehicle Trajectories Prediction in CARLA Urban Environment.2019back to text
• 51 inproceedingsH.Holger Caesar, V.Varun Bankiti, A. H.Alex H. Lang, S.Sourabh Vora, V. E.Venice Erin Liong, Q.Qiang Xu, A.Anush Krishnan, Y.Yu Pan, G.Giancarlo Baldan and O.Oscar Beijbom. nuScenes: A Multimodal Dataset for Autonomous Driving.Conference on Computer Vision and Pattern Recognition (CVPR)2020back to textback to textback to text
• 52 articleA. Q.Anh Quan Cao, G.Gilles Puy, A.Alexandre Boulch and R.Renaud Marlet. PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds.Submitted for publication2021back to textback to text
• 53 inproceedingsM.-F.Ming-Fang Chang, J. W.John W Lambert, P.Patsorn Sangkloy, J.Jagjeet Singh, S.Slawomir Bak, A.Andrew Hartnett, D.De Wang, P.Peter Carr, S.Simon Lucey, D.Deva Ramanan and J.James Hays. Argoverse: 3D Tracking and Forecasting with Rich Maps.Conference on Computer Vision and Pattern Recognition (CVPR)2019back to text
• 54 articleD.Dongliang Chang, A.Aneeshan Sain, Z.Zhanyu Ma, Y.-Z.Yi-Zhe Song and J.Jun Guo. Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation.arXiv preprint arXiv:2003.037872020back to text
• 55 articleC.Chenyi Chen, A.Ari Seff, A. L.Alain L. Kornhauser and J.Jianxiong Xiao. DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving.CoRR2015back to text
• 56 inproceedingsY.Yunjey Choi, Y.Youngjung Uh, J.Jaejun Yoo and J.-W.Jung-Woo Ha. Stargan v2: Diverse image synthesis for multiple domains.Conference on Computer Vision and Pattern Recognition (CVPR)2020back to text
• 57 articleD.Derek Christie, A.Anne Koymans, T.Thierry Chanard, J.-M.Jean-Marc Lasgouttes and V.Vincent Kaufmann. Pioneering Driverless Electric Vehicles in Europe: The City Automated Transport System (CATS).Transportation Research Procedia13Towards future innovative transport: visions, trends and methods, 43rd European Transport Conference Selected Proceedings2016, 30--39URL: http://www.sciencedirect.com/science/article/pii/S2352146516300047DOIback to text
• 58 inproceedingsL.Laurène Claussmann, A.Ashwin Carvalho and G.Georg Schildbach. A path planner for autonomous driving on highways using a human mimicry approach with Binary Decision Diagrams.European Control Conference (ECC)2015back to text
• 59 inproceedingsL.Laurène Claussmann, M.Marie O'Brien, S.Sébastien Glaser, H.Homayoun Najjaran and D.Dominique Gruyer. Multi-Criteria Decision Making for Autonomous Vehicles using Fuzzy Dempster-Shafer Reasoning.Intelligent Vehicles Symposium (IV)2018back to text
• 60 inproceedingsJ.J. Colyar and J.J. Halkias. Us highway 101 dataset..Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT07-0302007back to text
• 61 inproceedingsJ.J. Colyar and J.J. Halkias. Us highway i-80 dataset..Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT-06-1372006back to text
• 62 incollectionC.Charles Corbière, N.Nicolas Thome, A.Avner Bar-Hen, M.Matthieu Cord and P.Patrick Pérez. Addressing Failure Prediction by Learning Model Confidence.Advances in Neural Information Processing Systems (NeurIPS)Curran Associates, Inc.2019back to textback to text
• 63 inproceedingsS.Shumo Cui, B.Benjamin Seibold, R.Raphael Stern and D. B.Daniel B. Work. Stabilizing traffic flow via a single autonomous vehicle: possibilities and limitations.Intelligent Vehicles Symposium (IV)2017back to text
• 64 articleG.Guy Fayolle and J.-M.Jean-Marc Lasgouttes. Asymptotics and scalings for large closed product-form networks via the Central Limit Theorem.Markov Processes and Related Fields221996, 317-348back to text
• 65 articleG.Guy Fayolle, J.-M.Jean-Marc Lasgouttes and C.Carlos Flores. Stability and string stability of car-following models with reaction-time delay.SIAM Journal on Applied Mathematics8252022, 1661-1679HALDOIback to text
• 66 phdthesisC.Carlos Flores. Control architecture for adaptive and cooperative car-following.Université Paris sciences et lettresDecember 2018HALback to text
• 67 inproceedingsC.Carlos Flores, V.Vicente Milanés and F.Fawzi Nashashibi. Using Fractional Calculus for Cooperative Car-Following Control.Intelligent Transportation Systems Conference 2016IEEERio de Janeiro, BrazilNovember 2016HALback to text
• 68 inproceedingsM.Markus Forster, R.Raphael Frank, M.Mario Gerla and T.Thomas Engel. A Cooperative Advanced Driver Assistance System to mitigate vehicular traffic shock waves.INFOCOM - Conference on Computer Communications2014back to textback to text
• 69 articleG.Gianni Franchi, A.Andrei Bursuc, E.Emanuel Aldea, S.Séverine Dubuisson and I.Isabelle Bloch. Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification.arXiv preprint arXiv:2012.028182020back to text
• 70 inproceedingsG.Gianni Franchi, A.Andrei Bursuc, E.Emanuel Aldea, S.Séverine Dubuisson and I.Isabelle Bloch. TRADI: Tracking deep neural network weight distributions.European Conference on Computer Vision (ECCV)2020back to textback to text
• 71 inproceedingsC.Cyril Furtlehner, Y.Yufei Han, J.-M.Jean-Marc Lasgouttes, V.Victorin Martin, F.Fabrice Marchal and F.Fabien Moutarde. Spatial and Temporal Analysis of Traffic States on Large Scale Networks.Intelligent Transportation Systems Conference (ITSC)2010back to text
• 72 inproceedingsC.Cyril Furtlehner and J.-M.Jean-Marc Lasgouttes. A queueing theory approach for a multi-speed exclusion process..Traffic and Granular Flow '07Springer2009, 129-138URL: http://hal.archives-ouvertes.fr/hal-00175628/en/back to text
• 73 techreportC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes, A.Alessandro Attanasi, L.Lorenzo Meschini and M.Marco Pezzulla. Spatio-temporal Probabilistic Short-term Forecasting on Urban Networks.INRIA2018back to text
• 74 articleC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes and A.Anne Auger. Learning Multiple Belief Propagation Fixed Points for Real Time Inference.Physica A: Statistical Mechanics and its Applications2010back to text
• 75 inproceedingsC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes and A.Arnaud de La Fortelle. A belief propagation approach to traffic prediction using probe vehicles.Intelligent Transportation Systems Conference (ITSC)2007back to text
• 76 articleC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes and M.Maxim Samsonov. One-dimensional Particle Processes with Acceleration/Braking Asymmetry.Journal of Statistical Physics1476June 2012, 1113-1144HALDOIback to text
• 77 inproceedingsC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes and M.Maxim Samsonov. The Fundamental Diagram on the Ring Geometry for Particle Processes with Acceleration/Braking Asymmetry.TGF'11 - Traffic and Granular FlowMoscowDecember 2011, URL: http://hal.inria.fr/hal-00646988back to text
• 78 phdthesisF. J.Fernando Jose Garrido Carpio. Two-staged local trajectory planning based on optimal pre-planned curves interpolation for human-like driving in urban areas.Université Paris sciences et lettres2018back to text
• 79 articleF.Fernando Garrido, L.Leonardo González, V.Vicente Milanés, J.Joshué Pérez and F.Fawzi Nashashibi. A Two-Stage Real-Time Path Planning : Application to the Overtaking Manuever.IEEE AccessJuly 2020HALDOIback to text
• 80 inproceedingsJ. C.J. Christian Gerdes and S. M.Sarah M. Thornton. Implementable Ethics for Autonomous Vehicles.Autonomes Fahren: Technische, rechtliche und gesellschaftliche AspekteBerlin, HeidelbergSpringer Berlin Heidelberg2015, 87--102URL: https://doi.org/10.1007/978-3-662-45854-9_5DOIback to text
• 81 inproceedingsV.Vittorio Giammarino, M.Maolong Lv, S.Simone Baldi, P.Paolo Frasca and M. L.Maria L. Delle Monache. On a weaker notion of ring stability for mixed traffic with human-driven and autonomous vehicles.Conference on Decision and Control (CDC)2019back to text
• 82 inproceedingsB.Benjamin Graham, M.Martin Engelcke and L.Laurens Van Der Maaten. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks.Conference on Computer Vision and Pattern Recognition (CVPR)2018back to text
• 83 inproceedingsT.Tianyu Gu and J. M.John M. Dolan. On-Road Motion Planning for Autonomous Vehicles.Intelligent Robotics and Applications - International Conference, ICIRALecture Notes in Computer ScienceSpringer2012back to text
• 84 inproceedingsM.Mohamed Hadded, J.-M.Jean-Marc Lasgouttes, F.Fawzi Nashashibi and I.Ilias Xydias. Platoon Route Optimization for Picking up Automated Vehicles in an Urban Network.21st IEEE International Conference on Intelligent Transportation Systems2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC)Maui, United StatesNovember 2018HALback to text
• 85 articleM.Mohamed Hadded, P.Pascale Minet and J.-M.Jean-Marc Lasgouttes. A game theory-based route planning approach for automated vehicle collection.Concurrency and Computation: Practice and ExperienceFebruary 2021HALDOIback to text
• 86 articleJ. A.Joelle Al Hage, P.Philippe Xu, P.Philippe Bonnifait and J.Javier Ibanez-Guzman. Localization Integrity for Intelligent Vehicles Through Fault Detection and Position Error Characterization.Transactions on Intelligent Transportation Systems (T-ITS)2020back to text
• 87 articleZ.Zhenliang He, W.Wangmeng Zuo, M.Meina Kan, S.Shiguang Shan and X.Xilin Chen. Attgan: Facial attribute editing by only changing what you want.Transactions on Image Processing2019back to text
• 88 inproceedingsS.Simon Hecker, D.Dengxin Dai and L.Luc Van Gool. Failure prediction for autonomous driving.Intelligent Vehicles Symposium (IV)2018back to text
• 89 inproceedingsI.Isabell Hofstetter, M.Michael Sprunk, F.Frank Schuster, F.Florian Ries and M.Martin Haueis. On Ambiguities in Feature-Based Vehicle Localization and their A Priori Detection in Maps.Intelligent Vehicles Symposium (IV)2019back to text
• 90 articleY.Yue Hu and D. B.Daniel B. Work. Robust Tensor Recovery with Fiber Outliers for Traffic Events.Trans. Knowl. Discov. Data2021back to text
• 91 miscS.SAE International. SAE Standards: J3016 automated-driving graphic..back to text
• 92 articleJ.Joel Janai, F.Fatma Güney, A.Aseem Behl and A.Andreas Geiger. Computer vision for autonomous vehicles: Problems, datasets and state of the art.Foundations and Trends in Computer Graphics and Vision2020back to text
• 93 inproceedingsM.Maximilian Jaritz, R.Raoul De Charette, E.Emilie Wirbel, X.Xavier Perrotton and F.Fawzi Nashashibi. Sparse and dense data with CNNs: Depth completion and semantic segmentation.International Conference on 3D Vision (3DV)2018back to text
• 94 inproceedingsM.Maximilian Jaritz, T.-H.Tuan-Hung Vu, R. d.Raoul de Charette, E.Emilie Wirbel and P.Patrick Pérez. xmuda: Cross-modal unsupervised domain adaptation for 3D semantic segmentation.Conference on Computer Vision and Pattern Recognition (CVPR)2020back to textback to textback to text
• 95 articleB.Bernhard Kerbl, G.Georgios Kopanas, T.Thomas Leimkühler and G.George Drettakis. 3d gaussian splatting for real-time radiance field rendering..ACM Trans. Graph.4242023, 139--1back to text
• 96 articleP.Prannay Khosla, P.Piotr Teterwak, C.Chen Wang, A.Aaron Sarna, Y.Yonglong Tian, P.Phillip Isola, A.Aaron Maschinot, C.Ce Liu and D.Dilip Krishnan. Supervised contrastive learning.arXiv preprint arXiv:2004.113622020back to text
• 97 inproceedingsR.Robert Krajewski, J. B.Julian Bock qnd Laurent Kloeker and L.Lutz Eckstein. The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems.Intelligent Transportation Systems Conference (ITSC)2018back to text
• 98 inproceedingsY.Yoshiaki Kuwata, G. A.Gaston A. Fiore, J.Justin Teo, E.Emilio Frazzoli and J. P.Jonathan P. How. Motion planning for urban driving using RRT.International Conference on Intelligent Robots and Systems (IROS)2008back to text
• 99 miscM. M.MIT Media Lab. Moral Machine - Human Perspectives on Machine Ethics.back to text
• 100 inproceedingsP.-A.Pierre-Alain Langlois, A.Alexandre Boulch and R.Renaud Marlet. Surface reconstruction from 3D line segments.International Conference on 3D Vision (3DV)2019back to text
• 101 inproceedingsJ.-M.Jean-Marc Lasgouttes. Global On-line Optimization for Charging Station Allocation.Intelligent Transportation Systems Conference, ITSC 2015IEEE2015back to text
• 102 inproceedingsJ.-M.Jean-Marc Lasgouttes, A.Ahmed Soua and O.Oyunchimeg Shagdar. Toward Efficient Simulation Platform for Platoon Communication in Large Scale C-ITS Scenarios.IEEE International Symposium on Networks, Computers and CommunicationsRoma, ItalyJune 2018HALback to text
• 103 inproceedingsO.Olivier Le Marchand, P.Philippe Bonnifait, J.Javier Ibañez-Guzmán and D.David Betaille. Automotive localization integrity using proprioceptive and pseudo-ranges measurements.Accurate Localization for Land TransportationLes Collections de l'INRETS2009back to text
• 104 inproceedingsO.Olivier Le Marchand, P.Philippe Bonnifait, J.Javier Ibañez-Guzmán and D.David Betaille. Vehicle Localization Integrity Based on Trajectory Monitoring.Intelligent Robots and Systems (IROS)2009back to text
• 105 inproceedingsG.Guillaume Le Moing, T.-H.Tuan-Hung Vu, H.Himalaya Jain, M.Mathieu Cord and P.Patrick Pérez. Semantic Palette: Guiding Scene Generation with Class Proportions.Conference on Computer Vision and Pattern Recognition (CVPR)2021back to text
• 106 inproceedingsD.-H.Dong-Hyun Lee. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.Workshop on challenges in representation learning (ICML)2013back to text
• 107 articleS.Stéphanie Lefèvre, D.Dizan Vasquez and C.Christian Laugier. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH Journal112014, URL: http://www.robomechjournal.com/content/1/1/1DOIback to text
• 108 phdthesisE.Edouard Leurent. Safe and Efficient Reinforcement Learning for Behavioural Planning in Autonomous Driving.Université de Lille2020back to text
• 109 articleY.Yangyan Li, R.Rui Bu, M.Mingchao Sun, W.Wei Wu, X.Xinhan Di and B.Baoquan Chen. Pointcnn: Convolution on x-transformed points.Advances in Neural Information Processing Systems (NeurIPS)2018back to text
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