Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicentre of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output, and ranks them according to likelihood. To verify the method's performance and generalizability, we present a real-world dataset that consists of over 23,000 vehicles traversing five different intersections, collected using a vehicle mounted Lidar based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.
Driver simulators provide safe conditions to assess driver behaviour and provide controlled and repeatable environments for study. They are a promising research tool in terms of both providing safety and experimentally well controlled environments. There are wide ranges of driver simulators, from laptops to advanced technologies which are controlled by several computers in a real car mounted on platforms with six degrees of freedom of movement. The applicability of simulator-based research in a particular study needs to be considered before starting the study, to determine whether the use of a simulator is actually appropriate for the research. Given the wide range of driver simulators and their uses, it is important to know beforehand how closely the results from a driver simulator match results found in the real word. Comparison between drivers’ performance under real road conditions and in particular simulators is a fundamental part of validation. The important question is whether the results obtained in a simulator mirror real world results. In this paper, the results of the most recently conducted research into validity of simulators is presented. This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i.e., the possibility of multiple highly distinct futures, plays a critical role in decision making. We are motivated in this work by the example of traffic weaving, e.g., at highway on-ramps off-ramps, where entering and exiting cars must swap lanes in a short distance---a challenging negotiation even for experienced drivers due to the inherent multimodal uncertainty of who will pass whom. Our approach is to learn multimodal probability distributions over future human actions from a dataset of human-human exemplars and perform real-time robot policy construction in the resulting environment model through massively parallel sampling of human responses to candidate robot action sequences. Direct learning of these distributions is made possible by recent advances in the theory of conditional variational autoencoders (CVAEs), whereby we learn action distributions simultaneously conditioned on the present interaction history, as well as candidate future robot actions in order to take into account response dynamics. We demonstrate the efficacy of this approach with a human-in-the-loop simulation of a traffic weaving scenario.