A behavior driven approach for sampling rare event situations for autonomous vehicles

Performance evaluation of urban autonomous vehicles requires a realistic model of the behavior of other road users in the environment. Learning such models from data involves collecting naturalistic data of real-world human behavior. In many cases, acquisition of this data can be prohibitively expensive or intrusive. Additionally, the available data often contain only typical behaviors and exclude behaviors that are classified as rare events. To evaluate the performance of AV in such situations, we develop a model of traffic behavior based on the theory of bounded rationality. Based on the experiments performed on a large naturalistic driving data, we show that the developed model can be applied to estimate probability of rare events, as well as to generate new traffic situations

Long-lasting virtual motorcycle-riding trainer effectiveness

This work aimed to test the long-lasting effects of learning acquired with a virtual motorcycle-riding trainer as a tool to improve hazard perception. During the simulation, the rider can interact with other road actors and experience the most common potential accident situations in order to learn to modify his or her behavior to anticipate hazards and avoid crashes. We compared performance to the riding simulator of the two groups of participants: the experimental group, which was trained with the same simulator one year prior, and the control group that had not received any type of training with a riding or driving simulator. All of the participants had ridden a moped in the previous 12 months. The experimental group showed greater abilities to avoid accidents and recognize hazards in comparison to their performance observed a year before, whereas the performance of the control group was similar to that of the experimental group 1 year before in the first two sessions, and even better in the third. We interpreted this latter result as a consequence of their prior on-road experience. Also, the fact that the performance of the experimental group at the beginning of the follow-up is better than that recorded at the end of the training 1 year before is in line with the idea of a transfer from the on-road experience to the simulator. The present data confirm our main expectation that the effectiveness of the riding training simulator on the ability to cope with potentially dangerous situations persists over time and provides additional evidence in favor of the idea that simulators may be considered useful tools for training the ability to detect and react to hazards, leading to an improvement of this higher-order cognitive skill that persists over time. Implications for the reciprocal influence of the training with the simulator and the on-the road experience are discussed as well

Imitating Driver Behavior with Generative Adversarial Networks

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.Comment: 8 pages, 6 figure