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

A normal driving based deceleration behaviour study towards autonomous vehicles

Vehicle automation has recently attracted significant interest from the research community worldwide. Notwithstanding the remarkable development in autonomous vehicles (AVs), there is still a concern about the occupant's comfort since most research has mainly focused on the safety aspect. One of the most critical factors affecting the comfort level is the braking. It is however unclear which factors affect the braking behaviour and which braking profiles make the occupants feel safe and comfortable. This work therefore aims to thoroughly explore the deceleration behaviour of drivers using naturalistic driving study (NDS) data from two Field Operational Tests (FOT), the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project and the FOT conducted by Loughborough University and Nissan Ltd. A total of about 28 million observations were examined and almost 3,000 deceleration events from 37 different drivers and 174 different trips were identified and analysed. With the aid of a cluster analysis, a number of homogeneous scenarios based on human factors were formed. The scenarios have led to the application of multilevel mixed effect linear models to each cluster examining all influencing factors of the braking behaviour. The results indicate a dependence of the deceleration behaviour differing due to driver characteristics, initial speed and the reason for braking. Findings from this study will support vehicle manufacturers to ensure comfortable and safe braking operations of AV