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Crossing of Road Intersections : Decision-Making Under Uncertainty for Autonomous Vehicles

Abstract : Autonomous vehicles navigation implies that decisions are taken continuously based on a partial and uncertain knowledge of their environment. This is constrained by limited digital representations of the environment and uncertainty associated with the perception process. Further, it is difficult to predict the behaviour of the perceived entities. This is highlighted at crossroad intersections, where most road accidents occur. This thesis proposes a decision-making process that reasons with different types of uncertainties including the behaviour of the observed drivers to plan the vehicle motion.To understand the context and the behaviours of the observed driver's behaviour, a machine learning approach is proposed. The result is feed to the decision-making process to build probabilistic estimation of the environment. The vehicle motion is planned taking into account the effect this might have when the vehicle interact with other entities. Our approach rewards actions that promote interaction and reduces risk. The system behaviour is analysed by using a set of metrics derived from the scenario analysis as well as safety and operational constraints.To infer road context, Gaussian Processes are applied to learn motion patterns from simulated trajectories, which included the effect of vehicle interactions with other entities. The resulting patterns are segmented into areas and used to understand the behaviour of a vehicle approaching an intersection. Then, Random Forest Classifiers are applied to estimate the driver manoeuvre in each area. The dataset used for this training is built using data recorded from road trials and simulations. These classifiers infer lateral and longitudinal manoeuvre by extracting features from the vehicle trajectories. This approach shows that the road context improves the manoeuvre classification and that by mixing few real and simulated trajectories, it is possible to classify the manoeuvres of drivers arriving at an intersection.The decision-making process is built upon Partially Observable Markov Decision Process and uses the output of the manoeuvre understanding. This probabilistic framework reasons including perception and behaviour uncertainty in the environment models. These are used to predict the likely consequences of the autonomous vehicle actions on its immediate environment. However, the evaluation of all combinations of actions and state estimations is complex, therefore, an online solver is used to obtain an approximation of each action value. The vehicle actions are evaluated using a set of rewards, namely: collision risk, behavioural risk, comfort and traffic rules. A weighted sum of linear functions is used to balance each component of the reward function with respect to the vehicle distance to the intersection. It allows to adapt the behaviour of the automated vehicle to different scenarios.To validate the system performances and to determine causes of failure and success, Key Performance Indicators associated to the scenario are proposed. These are part of a generic testing architectures. The approach is applied to cross-cutting scenarios at road intersections, considered very complex and hazardous. Simulation techniques have been used to evaluate the proposed framework, to examine the largest number of scenarios and to be tested in safe conditions.The result of this thesis shows that it is possible to reason with other vehicle behaviour in the decision-making process while approaching a road intersection crossing. While classical methods fail to evaluate the system behaviour, the proposed validation method gives more insights. It will allow to test the system on the real road as well as using more advance data driven methods in the decision-making models.
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Submitted on : Tuesday, September 22, 2020 - 8:51:09 AM
Last modification on : Tuesday, October 6, 2020 - 4:20:06 PM


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  • HAL Id : tel-02945070, version 1




Mathieu Barbier. Crossing of Road Intersections : Decision-Making Under Uncertainty for Autonomous Vehicles. Artificial Intelligence [cs.AI]. Université Grenoble Alpes, 2019. English. ⟨NNT : 2019GREAM058⟩. ⟨tel-02945070⟩



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