Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote, minor correction in preliminarie

Multi-attribute decision making on mitigating a collision of an autonomous vehicle on motorways

Autonomous vehicles have the potential to improve automotive safety, largely by removing human error as a possible cause of collisions. However, it cannot be guaranteed that autonomous vehicles will be able to eliminate all collisions. Therefore, automotive safety will continue to be a necessity for automotive design. This paper proposes a decision making system which selects the least severe collision for an autonomous vehicle to take, when facing multiple imminent and unavoidable collisions on a motorway. The novel decision making system developed combines simulation results and multi-attribute decision making (MADM) methods. The simulator includes models of vehicle dynamics and the manoeuvre trajectory path. MADM methods are used to decide which vehicle(s) the autonomous vehicle should collide with, based on the severity of collisions. Severity of collisions is calculated in the simulator using the following variables: impact velocity between autonomous vehicle and vehicle ahead, impact velocity between vehicle behind and autonomous vehicle, manoeuvre acceleration and time-to-collision. Various MADM methods are investigated and three methods are selected including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Analytical Hierarchy Process (AHP), and the Analytical Network Process (ANP). Various collision scenarios are defined and tested in order to understand the impact that small changes in parameters of the autonomous vehicle and vehicles ahead and behind have on the decision made. The analysed decision making results are promising and lead to the conclusion that MADM methods can be successfully applied in autonomous vehicles