Evaluating Risk at Road Intersections by Detecting Conflicting Intentions

This work proposes a novel approach to risk assessment at road intersections. Unlike most approaches in the literature, it does not rely on trajectory prediction. Instead, dangerous situations are identified by comparing what drivers intend to do with what they are expected to do. What a driver intends to do is estimated from the motion of the vehicle, taking into account the layout of the intersection. What a driver is expected to do is derived from the current configuration of the vehicles and the traffic rules at the intersection. The proposed approach was validated in simulation and in field experiments using passenger vehicles and Vehicle-to-Vehicle communication. Different strategies are compared to actively avoid collisions if a dangerous situation is detected. The results show that the effectiveness of the strategies varies with the situation.Ces travaux proposent une nouvelle approche pour l'évaluation du risque aux intersections. Contrairement aux approches traditionnelles, celle-ci ne se base pas sur de la prédiction de trajectoire. A la place, les situations dangereuses sont identifiées en comparant ce que les conducteurs ont l'intention de faire avec ce qu'ils devraient faire. L'intention d'un conducteur est estimée à partir du mouvement de son véhicule et de l'agencement de l'intersection. Pour déterminer ce qu'un conducteur devrait faire, la configuration actuelle des véhicules dans la scène est prise en compte, ainsi que les règles de la circulation. L'approche proposée a été validée en simulation et au cours de tests réels avec des véhicules de série équipés de modems de communication V2V. Différentes stratégies sont comparées pour l'évitement de collision lorsqu'une situation dangereuse est détectée. Les résultats montrent que l'efficacité des stratégies varie avec la situation

Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning

This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed along its pre-defined trajectory to pass the crossing efficiently. Deep Q-learning is used on simulated traffic with different predefined driver behaviors and intentions. The results show a policy that is able to cross the intersection avoiding collision with other vehicles 98% of the time, while at the same time not being too passive. Moreover, inferring information over time is important to distinguish between different intentions and is shown by comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a Deep Q-learning at 1.75%.Comment: 6 pages, 7 figures, Accepted to IEEE International Conference on Intelligent Transportation Systems (ITSC) 201

Probabilistic Decision Making for Collision Avoidance Systems: Postponing Decisions

International audienceFor collision avoidance systems to be accepted by human drivers, it is important to keep the rate of unnecessary interventions very low. This is challenging since the decision to intervene or not is based on incomplete and uncertain information. The contribution of this paper is a decision making strategy for collision avoidance systems which allows the system to occasionally postpone a decision in order to collect more information. The problem is formulated in the framework of statistical decision theory, and the core of the algorithm is to run a preposterior analysis to estimate the benefit of deciding with the additional information. A final decision is made by comparing this benefit with the cost of delaying the intervention. The proposed approach is evaluated in simulation at a two-way stop road intersection for stop sign violation scenarios. The results show that the ability to postpone decisions leads to a significant reduction of false alarms and does not impair the ability of the collision avoidance system to prevent accidents

Impact of V2X privacy strategies on intersection collision avoidance systems

International audienceUser privacy is a requirement for wireless vehicular communications, and a number of privacy protection strategies have already been developed and standardized. In particular, methods relying on the use of temporary pseudonyms and silent periods have proved their ability to confuse attackers who would attempt to track vehicles. In addition to their ability to protect privacy, it is important to ensure that these privacy strategies do not hinder the safety applications which rely on vehicular communications. This paper addresses this concern and presents an experimental analysis of the impact of privacy strategies on Intersection Collision Avoidance (ICA) systems. We simulate traffic scenarios at a road intersection and compare the ability of a collision avoidance system to avoid collisions for different pseudonym change schemes. The privacy level is analyzed, as well as the influence of the duration of the silent period on the safety performance of the ICA system. The results highlight the need to jointly design safety applications and privacy strategies

Adaptive tactical behaviour planner for autonomous ground vehicle

Success of autonomous vehicle to effectively replace a human driver depends on its ability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through scenarios such as intersections that demand interactive behavior for successful navigation. The many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state of the art in path planning solutions. They have been found to result in inefficient and sometime unsafe behaviours when tackling interactively demanding scenarios. In this paper we review the current state of the art of path planning solutions, the individual planners and the associated methods for each planner. We then establish a gap in the path planning solutions by reviewing the methods against the objectives for successful path planning. A new adaptive tactical behaviour planner framework is then proposed to fill this gap. The behaviour planning framework is motivated by how expert human drivers plan their behaviours in interactive scenarios. Individual modules of the behaviour planner is then described with the description how it fits in the overall framework. Finally we discuss how this planner is expected to generate safe and efficient behaviors in complex dynamic traffic scenarios by considering a case of an un-signalised roundabout

Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning

This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed along its pre-defined trajectory to pass the crossing efficiently. Deep Q-learning is used on simulated traffic with different predefined driver behaviors and intentions. The results show a policy that is able to cross the intersection avoiding collision with other vehicles 98% of the time, while at the same time not being too passive. Moreover, inferring information over time is important to distinguish between different intentions and is shown by comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a Deep Q-learning at 1.75%

Impact of V2X privacy strategies on intersection collision avoidance systems

International audienceUser privacy is a requirement for wireless vehicular communications, and a number of privacy protection strategies have already been developed and standardized. In particular, methods relying on the use of temporary pseudonyms and silent periods have proved their ability to confuse attackers who would attempt to track vehicles. In addition to their ability to protect privacy, it is important to ensure that these privacy strategies do not hinder the safety applications which rely on vehicular communications. This paper addresses this concern and presents an experimental analysis of the impact of privacy strategies on Intersection Collision Avoidance (ICA) systems. We simulate traffic scenarios at a road intersection and compare the ability of a collision avoidance system to avoid collisions for different pseudonym change schemes. The privacy level is analyzed, as well as the influence of the duration of the silent period on the safety performance of the ICA system. The results highlight the need to jointly design safety applications and privacy strategies

Driver-centric Risk Object Identification

A massive number of traffic fatalities are due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in urgent need. Risky situations are generally defined based on collision prediction in existing research. However, collisions are only one type of risk in traffic scenarios. We believe a more generic definition is required. In this work, we propose a novel driver-centric definition of risk, i.e., risky objects influence driver behavior. Based on this definition, a new task called risk object identification is introduced. We formulate the task as a cause-effect problem and present a novel two-stage risk object identification framework, taking inspiration from models of situation awareness and causal inference. A driver-centric Risk Object Identification (ROI) dataset is curated to evaluate the proposed system. We demonstrate state-of-the-art risk object identification performance compared with strong baselines on the ROI dataset. In addition, we conduct extensive ablative studies to justify our design choices.Comment: Submitted to TPAM