Toward Reasoning of Driving Behavior
International Conference on Intelligent Transportation Systems (ITSC) 2018
Driving consists of a sequence of interaction with traffic environment and decision making based on situation understanding. Human drivers are capable of taking control in the complex situations smoothly. We thus believe understanding how humans drive and interact with traffic scenes is an important step to achieve an intelligent automated driving system. As the first step to achieve the ultimate goal, in this paper, we presented the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior and causal reasoning in real-life environments. The dataset includes 104 hours of naturalistic driving collected using an instrumented vehicle. We introduce an annotation scheme to describe complex driving behaviors. A baseline event detection algorithm based on Controller Area Network (CAN bus) data and the corresponding experiments are reported. Moreover, we provide a preliminary analysis on the cause and effect to the performance behavior detection.