Situation Understanding and Risk Assessment Framework for Preventive Driver Assistance. (Compréhension de situation et estimation de risques pour les aides à la conduite préventives)

Modern vehicles include advanced driving assistance systems for comfort and active safety features. Whilst these systems contribute to the reduction of road accidents, their deployment has shown that performance is constrained by their limited situation understanding capabilities. This is mainly due to perception constraints and by ignoring the context within which these vehicles evolve. It results in last minute risk assessment, and thus in curative assistance in the form of warning alerts or automatic braking. This thesis focuses on the introduction of contextual information into the decision processes of driving assistance systems. The overall purpose is to infer risk earlier than conventional driving assistance systems, as well as to enhance the level of trust on the information provided to drivers.Several factors govern the vehicle behaviour. These include the road network and traffic rules, as well as other road users such as vehicles and pedestrians with which the vehicle interacts. This results in strong interdependencies amongst all entities, which govern their behaviour. Further, whilst traffic rules apply equally to all participants, each driver interacts differently with the immediate environment, leading to different risk level for a given behaviour. This information must be incorporated within the decision-making processes of these systems. In this thesis, a framework is proposed that combines a priori information from digital navigation maps with real time information from on board vehicle sensors and/or external sources via wireless communications links, to infer a better situation understanding, which should enable to anticipate risks. This tenet is similar to the task of a co-pilot when using a priori notated road information. The proposed approach is constrained by using only data from close to production sensors. The framework proposed in this thesis consists of two phases, namely situation understanding and risk assessment.The situation understanding phase consists in performing a high level interpretation of all observations by including a priori information within the framework. The purpose is to understand how the perceived road entities interact, and how the interactions constrain the vehicle behaviour. This phase establishes the spatio-temporal relationships between the perceived entities to determine their relevance with respect to the subject vehicle motion, and then to identify which entities to be tracked. For this purpose, an ontology is proposed. It stores a priori information about the manner how different road entities relate and interact. This initial phase was tested in real time using data recorded on a passenger vehicle evolving in constrained environments.The risk assessment phase then looks into the perceived situation and into the manner how it becomes dangerous. To demonstrate the framework applicability, a use case applied to road intersections was chosen. Intersections are complex parts in the road network where different entities converge and most accidents occur. In order to detect risk situations, the manner how the driver reacts in a given situation is learned through Gaussian Processes. This knowledge about the driver is then used within a context aware Bayesian Network to estimate whether the driver is likely to interact as expected with the relevant entities or not. The probabilistic approach taken allows to take into consideration all uncertainties embedded in the observations. Field trials were performed using a passenger vehicle to validate the proposed approach. The results show that by incorporating drivers’ individualities and their actuations with the observation of the vehicle state, it is possible to better estimate whether the driver interacts as expected with the environment, and thus to anticipate risk. Further, it is shown that it is possible to generate assistance earlier than conventional safety systems.