Prediction of Driver Behavior and Decision Strategies for Autonomous Driving: Using Machine Learning and Decision Theory

Abstract

Autonomous and automated driving development has been making quick progress within the last years and many large automotive companies have announced initiatives to offer self-driving cars soon, some as early as 2020. During the first decades, these automated cars will have to share the road with human drivers. A way to increase safety and comfort for these automated vehicles and improve current advanced driver-assistance systems (ADAS), is to take the expected future behavior of other traffic participants into account. This increases the time a vehicle can use to react to changes in its environment and therefore allows for more comfortable or safe actions to be chosen. Relevant research areas to achieve this goal include computer vision, machine learning, situational awareness, decision making and many more. In this thesis, we contribute and evaluate new ideas for some of the challenges in the toolchain required for predicting the behavior of other traffic participants and deciding what to do with these. We used supervised machine learning techniques for both reactive predictions (short-term) and motivation-based predictions (long-term) and contribute to the question how the data used for machine learning can be labeled, comparing manual to semi-automated to automated approaches. This is especially interesting when subjective data is involved, e.g. at which point in time a motivation arose in a driver, even though he did not act on it yet. How to use and what to actually do with predictions in vehicles equipped with ADAS or automated vehicles is the next contribution. We evaluated many heuristics and a new, complex decision model based on decision theory. As use case, an adaptive cruise control system was enhanced by our prototype implementation of predictors and decision algorithms. We used an automated prototype research car in a case study and compared the effects on safety and comfort using objective data evaluation and subjective feedback from the study participants. We are able to show that even simple prediction and decision algorithms are able to improve the current status quo considerably and that the more advanced models work even better, but at the cost of substantial complexity increases.