Stochastic pedestrian models for autonomous vehicles

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.