2023Activity reportProject-TeamASTRA

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  47, Waymo Open  142, Woodscapes  153, SemanticKITTI  38, 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 37, 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.

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 43, 137. A large amount of naturalistic road user trajectories in different contexts (highways 56, 57, 97 or urban 47, 49) 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 46, 45.

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  63) 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  53, 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

• Kaouther Messaoud, Itheri Yahiaoui, Anne Verroust-Blondet and Fawzi Nashashibi received the Georges N. Saridis Best Transactions Paper Award for their paper Attention Based Vehicle Trajectory Prediction, IEEE Transactions on Intelligent Vehicles, 2021 (the awarded paper is selected among all papers published during the three calendar years preceding the year of the award).

6.1 New platforms

Participants: Paulo Resende, Benazouz BRADAI, Gaëtan Le Gall, Fawzi Nashashibi.

The creation of the team has resulted in the strengthening ofthe experimental side of the team which has always had among its objectives the validation of work on real instrumented platforms. The creation of the team has resulted in the strengthening of the experimental side of the team which has always had among its objectives the validation of work on real instrumented platforms. Thus, the team is equipped with at least four road vehicles (Cruise4U and Drive4U from Valeo, a C1 and a Zoé from Inria), 3 shuttles (2 Cybus from Inria and a NAVYA from Valeo) and 2 Cybercars (Inria) (figure 6).

7.1 3D scene reconstruction and completion

Participants: Anh-Quan Cao, Raoul de Charette.

In this research axis, within the context of Anh Quan Cao PhD thesis, we have studied the ability to estimate the 3D scene geometry and semantics from images or 3D data. In SceneRF 20, we introduced a self-supervised method for monocular 3D scene reconstruction using Neural Radiance Fields (NeRF), trained solely from multiple posed image sequences. To enhance geometry prediction, we present new geometrical constraints and a novel probabilistic sampling strategy for effective radiance fields training. The performance surpasses the state-of-the-art on multiple benchmarks and metrics. In a collaborative work with the Technical University of Munich (TUM), we proposed a method named PaSCo 29. The latter investigates the popular Semantic Scene Completion (SSC) task, also addressed in multiple works of the team, to incorporate uncertainty estimation. Here, we extended SSC to the Panoptic Scene Completion (PSC) task, to include instance-level information, resulting in richer 3D scene understanding. Crucially, we noticed that the literature overlooks uncertainty estimation despite it's crucial importance for safety-critical application like autonomous driving. To allow uncertainty-awareness, our approach follows utilize a multi-input multi-output architecture to predict variations of the PSC output and therefore estimate the voxel-wise and instance-wise uncertainty. Experiments show our method outperforms all baselines in both Panoptic Scene Completion and uncertainty estimation on three large-scale benchmarks.

SceneRF was presented at ICCV 2023 and PaSCo is accepted at CVPR 2024 29. Both works are shared opensource.

7.2 Cross-task learning for vision algorithms

Participants: Ivan Lopes, Tuan-Hung Vu, Raoul de Charette.

In 25 we explore the ability to learn multiple dense prediction tasks to train a more robust network. Specifically, we jointly address semantic and geometry-related tasks which are known to be complementary, by leveraging attention mechanisms to enforce cross-task exchange. Extensive experiments are conducted on three diverse multi-task setups, we show their benefit across all tasks on both indoor and outdoor datasets. This work, named DenseMTL, is opensource and published in WACV 2023.

7.3 Physics-guided learning vision

Participants: Ivan Lopes, Tan Khiem Huynh, Raoul de Charette.

A major axis of the vision group is the study of machine learning under physics guidance. We explore two major direction: 1) the use of physics prior to solve vision, 2) the use of vision algorithms to discover physics. In the first one, the team had major works in the past.

1) The use of physics knowledge for vision was extensively studied in the team, in particular in past works 14, 13, 16, 7.

Recently, we addressed a novel problem of extraction of materials from an image in the context of Ivan Lopes PhD thesis in a joint work with a collaborator from Oxford University. Following the common practices in computer graphics, we formulate materials as Physically Based Rendering (PBR) to model the interaction between materials and light sources. Because such PBR materials are hard to acquire, in Material Palette 32, we propose the first method to extract PBR materials from a single real-world image. To do so, we leveraged a large-scale diffusion model and training a multi-task network to decompose textures. In addition to opensource libraries, we created a custom synthetically generated high-quality dataset of 9,000 RGB textures, TexSD, openly shared to the public. The complete method sets a new state-of-the-art. We also showcase the capabilities to edit 3D scenes with materials estimated from real photographs.

In another direction, with Weihao Xia (PhD visit student) in collaboration with University College London (UCL) and Cambridge, we introduced DREAM 27, a method for visual decoding using a biologically-inspired architecture. In particular, we studied the ability to decode the brain activities of a patient when looking at an images — thus reconstructing the image from fMRI data. This task, refered as visual decoding, is usually addressed using the support of language. In DREAM, we reason on biological principle, designing an architecture that reverses the human pathways to decipher geometry, color and semantics. The resulting method outperformed the literature.

2) Another prospective direction is the use of vision algorithms to discover physics models. In particular we seek here to estimate the symbolic models ruling light propagation. This challenging research direction, was initiated with an intern, focusing first on the discovery of the simple light attenuation models in fog. There exist physicals model for atmospheric scattering, one commonly used estimates fog from a clear image $I_0$ as $I\left(d\right)=I_0{e}^{-\beta d}+L_{\infty }(1-e^{-\beta d})$; where $d$, $\beta$, and $L_{\infty }$ are the depth map, scattering coefficient, and atmospheric light, respectively. The method employed used a sequence of images under foggy conditions to unsupervisedly discover the light attenuation models, by tracking the luminance of salient points in the video. Although the project was not finalized, the groundwork accomplished during the internship provides the foundation for a potential future publication.

In this axis, DREAM27 is published at WACV 2024 and Material Palette32 is accepted at CVPR 2024. Another work not detailed here is the journal 19,which extends a prior work and was recently accepted in T-PAMI. All works are opensource.

7.4 Language-driven vision

Participants: Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette.

In this axis, in the context of Mohammad Fahes PhD thesis, we address a major problem of deep-learning based computer vision algorithms which is their lack of generalization to out-of-distribution samples. Here, we build on recent advances in Vision-Language Model (VLM) to adapt or generalize the performance of a model.

In PODA 21 we propose a setting of “prompt-driven zero-shot domain adaptation”, where the only target information is reduced to a description of the target domain in natural language (e.g. driving at night). Our research builds on multimodal vision-language space (here, CLIP), to optimize the features of a network, by steering them towards a target text embedding while preserving the features content and semantics. Such augmented features can serve any proxy tasks, and our work demonstrates strong performances in semantic segmentation, object detection and classification problems.

Rather than adaptation to a specific domain, in FAMix 30 we tackle the question of generalizing a model trained on a source domain to any potential unseen domain. For instance, we were interested in training a segmentation model on synthetic driving scenes images from a game engine (GTA5) to real-world driving scenes — significantly more complex to acquire. With FAMix, we introduced a simple framework for generalizing semantic segmentation networks by employing language as the source of randomization. Our recipe comprises three key ingredients: i) the preservation of the intrinsic CLIP robustness through minimal fine-tuning, ii) language-driven local style augmentation, and iii) randomization by locally mixing the source and augmented styles during training. Extensive experiments report state-of-the-art results on various generalization benchmarks.

The work PODA21 is published at ICCV 2023 and FaMix30 is at CVPR 2024. An extension of the former work is next to ready for a submission in a journal. All works are shared opensource.

7.5 Misbehavior Detection for Collective Perception Services: A Systematic Trust-Based Evidence Approach

Participants: Jiahao Zhang, Fawzi Nashashibi.

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

Collective perception services (CPS) allow vehicles to extend their perception of the environment beyond their Field of View through V2X communication. To build an extended perception system, received objects are fused with the local perceived objects. The association process is a crucial step in the perceptual data fusion. We leverage the perception data association results to detect misbehaving nodes when contradictory perceived information are received. Especially, we design and implement a misbehavior detection architecture for CPS relying on an evidential object association approach 61. The result of the association process is used to assess the node trust worthiness through the subjective logic mechanism 95.

7.6 Hierarchical Attention and Graph Neural Networks for Drift-Free Pose Estimation of a ground vehicle

Participants: Fawzi Nashashibi.

This work has been conducted in collaboration with Kathia Melbouci after her departure from ASTRA at the end of 2022.

We explored an architecture that replaces traditional geometric registration and pose graph optimization of a ground veicle with a learned model utilizing attention mechanisms and graph neural networks. In a paper submitted to IEEE ICRA 2024, We propose a strategy to condense the data flow, preserving essential information required for the precise estimation of rigid poses. Our results, derived from tests on the KITTI Odometry dataset, demonstrate a significant improvement in pose estimation accuracy. This improvement is especially notable in determining rotational components when compared with results obtained through conventional multi-way registration via pose graph optimization. The corresponding code will be made available as soon as the paper status is determined.

7.7 Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios

Participants: Amina Ghoul, Itheri Yahiaoui, Fawzi Nashashibi.

The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to explain because of their uncertainty. In recent years, neural network-based methods have been widely used for trajectory prediction and have been shown to outperform hand-crafted methods. However, these methods suffer from their lack of interpretability. In order to overcome this limitation, we combine in 23 the interpretability of a discrete choice model with the high accuracy of a neural network-based model for the task of vehicle trajectory prediction in an interactive environment. We implement and evaluate our model using the INTERACTION dataset and demonstrate the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.

7.8 Interpretable Long Term Waypoint-Based Trajectory Prediction Model

Participants: Amina Ghoul, Itheri Yahiaoui, Fawzi Nashashibi.

Predicting the future trajectories of dynamic agents in complex environments is crucial for a variety of applications, including autonomous driving, robotics, and human-computer interaction. It is a challenging task as the behavior of the agent is unknown and intrinsically multimodal. Our key insight is that the agents behaviors are influenced not only by their past trajectories and their interaction with their immediate environment but also largely with their long term waypoint (LTW). In 22, we study the impact of adding a long-term goal on the performance of a trajectory prediction framework. We present an interpretable long term waypoint-driven prediction framework (WayDCM). WayDCM first predict an agent's intermediate goal (IG) by encoding his interactions with the environment as well as his LTW using a combination of a Discrete choice Model (DCM) and a Neural Network model (NN). Then, our model predicts the corresponding trajectories. This is in contrast to previous work which does not consider the ultimate intent of the agent to predict his trajectory. We evaluate and show the effectiveness of our approach on the Waymo Open dataset.

7.9 Motion planning and prediction

Participants: Nelson De Moura, Augustin Gervreau.

The study of different longitudinal behaviors for vehicles was completely restructured during 2023, resulting in a two-step method based on the DTW dissimilarity matrix clustering with k-means, which was published in 26. However, for the longitudinal clustering, so as to extract the different behaviors, another method had to be created, that considers the dynamic characteristics of each vehicle. This work will be publish in 2024.

For pedestrians and cyclists the main difficulty is to filter the outlier trajectories and the ones that are not representative of a overall behavior. This has been done with hierarchical clustering considering the shape and the initial and final points for clustering. This work will also be published in 2024. And from these results the first simulation agent models started to been trained.

7.10 Control and Human Factors

Participants: Nelson De Moura, Tiago Rocha.

Following prior member works 123, 60, we studied a risk prediction system to evaluate the trade-off between ethical aspects and fuel-efficiency improvements in platooning systems. Leveraging the harm concept from 123, an MDP approach is used to adjust dynamically controllers during platooning operation. Results published in 24.

7.11 Ethical decision-making for automated vehicles

Participants: Nelson De Moura.

The ethics of automated vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. After a discussion about the pertinence and cogency of the term “artificial moral agent” to describe AVs that would accomplish these sorts of decisions, and starting from the assumption that human harm is unavoidable in some situations, a strategy for AV decision making is proposed using only pre-defined parameters to characterize the risk of possible accidents and also integrating the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation, into multiple possible decision rules to determine the most suitable action given the specific environment and decision context. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of human “moral positions” concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an automated vehicle's decision making. Published in 28

7.12 Landmark localization for Autonomous Vehicles

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

We are interested in the case where we have a map in an urban environment, with landmarks whose position is known, up to some Gaussian error. A vehicle, equipped with sensors to detect such landmarks, must determine its position based on its knowledge of the map and its observations. The performance of the proposed algorithm enables it to be used in real time at a speed consistent with the hypothesis of an urban environment (30 to 50 km/h), and the accuracy of the localization performed (error of less than 10 cm) is good enough to enable it to be used by an autonomous vehicle.

7.13 Shock Wave Estimation in Intelligent Driver Models

Participants: Guy Fayolle, Jean-Marc Lasgouttes.

We investigate the transfer function $Q\left(z\right)$ emanating from the linearization of a car-following model for human drivers, when taking into account their reaction time, which is known to be a cause of the stop-and-go traffic phenomenon. This leads to a non rational transfer function, implying nontrivial stability conditions which are explicitly given. They are in particular satisfied whenever string stability holds. It is also shown how this reaction time can introduce a regime of partial string stability, where the transfer function modulus remains smaller than 1, up to some critical frequency. Conditions are explored in the parameter space discriminating between 4 different regimes (instability, string stability, partial string stability, string instability).

Starting from the study 64, we aim first to describe the schockwaves that may form in the string instability regime. To this end, we are currently analyzing the transfer function $Q{\left(z\right)}^N$, for of a string of $N$ vehicles, $N$ sufficiently large. This preliminary step requires a sharp analysis, for which the usual saddle point methods seem not to work.

7.14 Reflected brownian motion in a non convex cone

Participants: Guy Fayolle.

G. Fayolle, in collaboration with S. Franceschi (LMO, Paris-Saclay University) and K Raschel (CNRS, Tours University), study the stationary reflected Brownian motion in a non-convex wedge, which, compared to its convex analogue model, has been much rarely analyzed in the probabilistic literature. Two approaches are proposed for the three-quarter plane. In 62, it was proved that the stationary distribution could be found by solving a two dimensional vector boundary value problem (BVP) on a single curve (an hyperbola) for the associated Laplace transforms. The reduction to this kind of vector BVP seems to be quite new in the literature. As a matter of comparison, one single boundary condition is sufficient in the convex case. When the parameters of the model (drift, reflection angles and covariance matrix) are symmetric with respect to the bisector line of the cone, the model is reducible to a standard reflected Brownian motion in a convex cone. Finally, we construct a one-parameter family of distributions, which surprisingly provides, for any wedge (convex or not), one particular example of stationary distribution of a reflected Brownian motion.

In 17, the main result is to show that the stationary distribution can in fact be obtained by solving a boundary value problem of the same kind as the one encountered in the quarter plane, up to various dualities and symmetries. The idea is to start from Fourier (and not Laplace) transforms, allowing to get a functional equation for a single function of two complex variables.

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

Participants: Guy Fayolle.

Following their previous analysis 65 G. Fayolle and P. Mühlethaler 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. As in previous studies, the key point of our 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 31.

7.16 Blockchain adapted to IoT via green mining and variable Proof of Work

Participants: Guy Fayolle.

Blockchain applications continue to grow in popularity, but their energy costs are clearly becoming unsustainable. In most cases, the primary cost comes from the amount of energy required for proof-of-work (PoW). G. Fayolle and Ph. Jacquet study the application of blockchains to the IoT, where most devices are underpowered and would not support the energy cost of proof of work. PoW was originally intended for two main uses: block moderation and protecting the blockchain from tampering. For IoT we suggest to replace the expensive moderation of PoW with the proposed energy-efficient green mining. The blockchain will be protected by a variable difficulty PoW. One of the results is the proof that the average mining time in variable PoW actually depends only on the average difficulty. This crucial property will allow low-difficulty PoWs to be reserved for devices with low computational capacity, while higher difficulties will be reserved for devices with the highest computational power. The consequence is to give equal opportunity to objects with low computational power compared to objects with high computational power.

7.17 Random walks in orthants and lattice path combinatorics

Participants: Guy Fayolle.

In the revised version of the second edition of the book 4 (see also the corrections), original methods were proposed to determine the invariant measure of random walks in the quarter plane with small jumps (size 1), the general solution being obtained via reduction to boundary value problems. Among other things, an important quantity, the so-called group of the walk, allows to deduce theoretical features about the nature of the solutions. In particular, when the order of the group is finite and the underlying algebraic curve is of genus 0 or 1, necessary and sufficient conditions have been given for the solution to be rational, algebraic or $D$-finite (i.e. satisfying a linear differential equation). In this framework, number of difficult open problems related to lattice path combinatorics are currently being explored by Guy fayolle, in collaboration with A. Bostan and F. Chyzak, both from theoretical and computer algebra points of view: concrete computation of the criteria, utilization of differential Galois theory, genus greater than 1 (i.e. when some jumps are of size $\ge 2$), etc. A recent topic (mentioned in 2019) deals with the connections between simple product-form stochastic networks (so-called Jackson networks) and explicit solutions of functional equations for counting lattice walks. Some partial extensions of this earlier work are still under development.

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 Martins Gomes , 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 2023 the collaboration led to 3 open source realizations, 3 top-tier publications and the co-supervision of 1 internship, 2 PhDs.

9.1 International initiatives

Palestine Polytechnic University: This a new-born collaboration between the Intelligent Systems Research Centre of PPU and ASTRA team. The academic collaborations include:

• Bilateral academic visits
• Hosting and recruiting doctoral and post-doctoral PPU students
• Participation in joint National/Horizon collaborative projects
• Involvement in teaching & education (lectures, trainings mentorships, keynotes...)

9.2 International research visitors

9.3 National initiatives

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 2022HAL
• 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 2023HAL
• 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, 255HALDOIback to text
• 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

• 33 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
• 34 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
• 35 articleA.Arjun Balakrishnan, S. R.Sergio Rodriguez Florez and R.Roger Reynaud. Integrity Monitoring of Multimodal Perception System for Vehicle Localization.Sensors2020back to text
• 36 phdthesisP.Pierre de Beaucorps. Planification de trajectoire dans un environnement peu contraint et fortement dynamique.Sorbonne Université2019back to text
• 37 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
• 38 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
• 39 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
• 40 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
• 41 articleA.Alexandre Boulch. ConvPoint: Continuous convolutions for point cloud processing.Computers & Graphics2020back to textback to text
• 42 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
• 43 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
• 44 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
• 45 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
• 46 miscT.Thibault Buhet, E.Emilie Wirbel and X.Xavier Perrotton. Conditional Vehicle Trajectories Prediction in CARLA Urban Environment.2019back to text
• 47 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
• 48 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
• 49 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
• 50 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
• 51 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
• 52 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
• 53 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
• 54 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
• 55 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
• 56 inproceedingsJ.J. Colyar and J.J. Halkias. Us highway 101 dataset..Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT07-0302007back to text
• 57 inproceedingsJ.J. Colyar and J.J. Halkias. Us highway i-80 dataset..Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT-06-1372006back to text
• 58 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
• 59 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
• 60 articleR. F.Rafael F. Cunha, T. R.Tiago R. Gonçalves, V. S.Vineeth S. Varma, S. E.Salah E. Elayoubi and M.Ming Cao. Reducing fuel consumption in platooning systems through reinforcement learning.IFAC-PapersOnLine55156th IFAC Conference on Intelligent Control and Automation Sciences ICONS 20222022, 99-104URL: https://www.sciencedirect.com/science/article/pii/S2405896322010266DOIback to text
• 61 articleT.Thierry Denoux, N. E.Nicole El Zoghby, V.Veronique Cherfaoui and A.Antoine Jouglet. Optimal object association in the dempster-shafer framework.IEEE Transactions on Cybernetics44122014, 2521--2531DOIback to text
• 62 articleG.Guy Fayolle, S.Sandro Franceschi and K.Kilian Raschel. On the stationary distribution of reflected Brownian motion in a non-convex wedge.Markov Processes And Related Fields285December 2022, 32HALback to text
• 63 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
• 64 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 textback to text
• 65 articleG.Guy Fayolle and P.Paul Mühlethaler. A Markovian Analysis of IEEE 802.11 Broadcast Transmission Networks with Buffering.Probability in the Engineering and Informational Sciences303June 2016, 19HALDOIback 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 bookA.A. Jøsang. Subjective Logic: A Formalism for Reasoning Under Uncertainty.Artificial Intelligence: Foundations, Theory, and AlgorithmsSpringer International Publishing2016, URL: https://books.google.fr/books?id=nqRlDQAAQBAJback 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
• 110 inproceedingsN.Nan Li, H.Hao Chen, I.Ilya Kolmanovsky and A.Anouck Girard. An Explicit Decision Tree Approach for Automated Driving.ASME 2017 Dynamic Systems and Control ConferenceDynamic Systems and Control Conference2017back to text
• 111 articleN. I.Nan I. Li, D. W.Dave W. Oyler, M.Mengxuan Zhang, Y.Yildiray Yildiz, I. V.Ilya V. Kolmanovsky and A. R.Anouck R. Girard. Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems.CoRR2016back to text
• 112 articleC.Chengyao Li and S. L.Steven L. Waslander. Visual Measurement Integrity Monitoring for UAV Localization.CoRR2019back to text
• 113 articleM.Maxim Likhachev and D.Dave Ferguson. Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles.International Journal of Robotics Research2009back to text
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