Stochastic pedestrian models for autonomous vehicles
Creators: name: Hartmann, Michael
Publisher: name: figshare
Year: 2022
Subjects: subject: 91303 Autonomous Vehicles
subject: FOS: Mechanical engineering

description: This dissertation addresses the modeling of pedestrians in dynamic and urban environments interacting with autonomous vehicles. The collision avoidance system of an autonomous vehicle has contrary safety and effi- ciency requirements. On the one hand, there might be collisions in following a risky driving policy. On the other hand, a safe driving policy might bring the passenger very slow to the target to prevent all kinds of risks. The au- tonomous vehicle does not know or perceive all relevant information, such as the unknown intention and the environmental and situational factors influencing the pedestrian’s behavior. There is a resulting decision dilemma for autonomous vehicles between road safety for all road users and efficient motion planning in environments with vulnerable road users. There also exists a lack of knowledge by predicting the future movements of pedestri- ans, where one could compute worst-case reachable state-sets. The areas of possible reach sets could get very large. An autonomous vehicle is not allowed to drive into these areas, making motion planning inefficient. The adaption to real-world scenarios is not trivial. The decision-making process in motion planning is challenging due to the enormous variety of situations and the uncertainty of predicting future human movements with absolute certainty. There is a potential risk of accidents in adapting and predicting human locomotion. These problems influence the trust and acceptance of autonomous vehicles with additional technological and legal challenges. This dissertation aims not to ensure total safety because of pedestrians’ technical and diverse physical, cognitive, situational, and environmental complexity. This work uses a new method that combines machine learning with reachability analysis (resulting in an adaptive funnel, hull, or belief set computation). Machine learning adapts the reachability analysis to current situations. Therefore adaptive reachability analysis and corresponding mo- tion planning are presented and evaluated in vehicle simulations. Adaptive hull computational methods for adaptive implementation of reachabilianalysis lead to risky pedestrian bypassing. These computational methods provide a trade-off between safety and efficiency in motion planning. How- ever, the proposed approach cannot guarantee an exact threshold for overall safety due to the complexity of the problem (unknown intent and environ- mental factors). A very intuitive approach predicts the future situation’s maximal velocity, acceleration, and jerk of a pedestrian. Afterward, it com- putes the adaptive reach sets with conventional methods. Adapting classical worst-case reachability analysis could drastically reduce the cumulative volume of adaptive reachable sets compared to classical reachable sets (less than 70 percent of the cumulative area compared to classical computation in a presented use case). The results show a massive potential to reduce the areas from reach sets to belief sets. The causal inference could model the intention change. Intent changes are modeled with causal inference without considering structural learning and tested with model predictive control and adaptive hull computation in a simulation environment. This thesis also presents a similarity of human locomotion with (Partially Observ- able) Markov Decision Processes (MDPs and POMDPs). Nevertheless, many causal relationships describing pedestrian behavior are still unexplored. Concepts for building novel test environments provide an outlook on fun- damental research. New test environments might bring new insights into the causal information structure between cognition and human locomotion in different settings to improve accident statistics further. These new test environments could help make the adaptive reachable sets more robust and provide a quantitive guarantee for trusting novel hull computations. <br>