Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches

The growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architectures with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks. However, the lack of safety guarantees proves, which is one of the key challenges to be addressed, limit drastically the ambition to introduce more broadly AVs on our roads and restrict the use of AVs to very limited use cases. Therefore, the focus and the ambition of this paper is to survey research on autonomous vehicles while focusing on the important topic of safety guarantee of AVs. For this purpose, it is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs, with an emphasis on the safety assessment and decision-making systems composing these architectures. Moreover, it is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches. This is performed while emphasizing the strengths and weaknesses of each methodology and investigating the research that proposes a comprehensive multi-modal design that combines model-based and AI approaches. This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks

Trajectoires sûres et architecture bayésienne séquentielle de prise de décision pour une navigation autonome fiable des véhicules

Recent advances in Autonomous Vehicles (AV) driving raised up all the importance to ensure the complete reliability of AV maneuvers even in highly dynamic and uncertain environments/situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architecture with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks (theoretically, lower than any human-like driving behavior), with a systemic way. Consequently, the aim is also to reduce the need for too extensive testing (which could take several months and years for each produced RAMS without at the end having absolute prove). Hence the goal in this Ph.D. thesis is to have a provable methodology for AV RAMS. This dissertation addresses the full pipeline from risk assessment, path planning to decision-making and control of autonomous vehicles. In the first place, an overall Probabilistic Multi-Controller Architecture (P-MCA) is designed for safe autonomous driving under uncertainties. The P-MCA is composed of several interconnected modules that are responsible for: assessing the collision risk with all observed vehicles while considering their trajectories' predictions; planning the different driving maneuvers; making the decision on the most suitable actions to achieve; control the vehicle movement; aborting safely the engaged maneuver if necessary (due for instance to a sudden change in the environment); and as last resort planning evasive actions if there is no other choice. The proposed risk assessment is based on a dual-safety stage strategy. The first stage analyzes the actual driving situation and predicts potential collisions. This is performed while taking into consideration several dynamic constraints and traffic conditions that are known at the time of planning. The second stage is applied in real-time, during the maneuver achievement, where a safety verification mechanism is activated to quantify the risks and the criticality of the driving situation beyond the remaining time to achieve the maneuver. The decision-making strategy is based on a Sequential Decision Networks for Maneuver Selection and Verification (SDN-MSV) and corresponds to an important module of the P-MCA. This module is designed to manage several road maneuvers under uncertainties. It utilizes the defined safety stages assessment to propose discrete actions that allow to: derive appropriate maneuvers in a given traffic situation and provide a safety retrospection that updates in real-time the ego-vehicle movements according to the environment dynamic, in order to face any sudden hazardous and risky situation. In the latter case, it is proposed to compute the corresponding low-level control based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) that allows the ego-vehicle to pursue the advised collision-free evasive trajectory to avert an accident and to guarantee safety at any time.The reliability and the flexibility of the overall proposed P-MCA and its elementary components have been intensively validated, first in simulated traffic conditions, with various driving scenarios, and secondly, in real-time with the autonomous vehicles available at Institut Pascal.Les dernières avancées en matière de conduite de véhicules autonomes (VAs) ont fait apparaître toute l'importance de garantir la fiabilité complète des manœuvres que doivent effectuer les VAs, y compris dans des environnements/situations très dynamiques et incertains. Cet objectif devient encore plus ardu en raison du caractère unique de chaque situation/condition de circulation. Pour faire face à toutes ces configurations très contraignantes et complexes, les VAs doivent disposer d'une architecture de contrôle appropriée avec des Stratégies d'Evaluation et de Gestion des Risques (SEGR) fonctionnant en temps-réel et d'une manière fiable. Ces SEGR ciblées doivent conduire à une réduction drastique des risques de conduite. Théoriquement et de maniéré systémique, ces SEGR doivent aboutir à un risque de conduite inférieur à tout comportement de conduite humaine. En conséquent, il est également question de réduire la nécessité d'effectuer des tests très poussés, qui peuvent prendre plusieurs mois/années pour au final ne pas avoir de preuves formelles de la viabilité et de la sûreté complète du système. Ainsi, les travaux présentés dans cette thèse de doctorat ont pour but d'avoir une méthodologie prouvable pour les SGER des VAs.Cette thèse porte sur l'ensemble du processus, en partant de l'évaluation des risques, de la planification de la trajectoire jusqu'à la prise de décision et au contrôle du véhicule autonome. En premier lieu, une architecture multi-contrôleurs probabiliste (Probabilistic Multi-Controller Architecture P-MCA) est conçue pour une conduite autonome sûre en présence d'incertitudes. Cette architecture est composé de plusieurs modules interconnectés qui sont responsables de : l'évaluation du risque de collision avec tous les véhicules observés tout en considérant les prévisions de leurs trajectoires ; la planification des différentes manœuvres de conduite ; la prise de décision sur les actions les plus appropriées à réaliser ; le contrôle du mouvement du véhicule ; l'interruption en toute sécurité de la manœuvre engagée si nécessaire (en raison par exemple d'un changement soudain de l'environnement routier) ; et en dernier recours la planification des actions évasives à défaut d'un autre choix. L'évaluation des risques proposée est basée sur une stratégie à deux étapes. La première étape consiste à analyser la situation actuelle de conduite et à prévoir les éventuelles collisions. Cette étape est réalisée en tenant compte de plusieurs contraintes dynamiques et des conditions de circulation connues au moment de la planification. La deuxième étape est appliquée en temps-réel, durant la réalisation de la manœuvre, où un mécanisme de vérification de la sécurité est activé pour quantifier les risques et la criticité de la situation de conduite sur le temps restant pour réaliser la manœuvre. La stratégie décisionnelle est basée sur un réseau Bayésien de décision à niveaux séquentiels pour la sélection et la vérification des manœuvres (Sequential Decision Networks for Maneuver Selection and Verification SDN-MSV) et constitue un module essentiel de l'architecture P-MCA. Ce module est conçu pour gérer plusieurs manœuvres routières dans un environnement incertain. Il utilise l'évaluation des étapes de sécurité définies pour proposer des actions discrètes qui permettent de : réaliser des manœuvres appropriées dans une situation de trafic donnée, il fournit également une rétrospective de la sécurité, cette dernière actualise en temps-réel les mouvements de l'égo-véhicule en fonction de la dynamique de l'environnement, afin de faire face à toute situation dangereuse et risquée soudaine. (...

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Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches

International audienceThe growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architectures with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks. However, the lack of safety guarantees proves, which is one of the key challenges to be addressed, limit drastically the ambition to introduce more broadly AVs on our roads and restrict the use of AVs to very limited use cases. Therefore, the focus and the ambition of this paper is to survey research on autonomous vehicles while focusing on the important topic of safety guarantee of AVs. For this purpose, it is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs, with an emphasis on the safety assessment and decision-making systems composing these architectures. Moreover, it is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches. This is performed while emphasizing the strengths and weaknesses of each methodology and investigating the research that proposes a comprehensive multi-modal design that combines model-based and AI approaches. This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks

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