Lane Intrusion Behaviors Dataset: Action Recognition in Real-world Highway Scenarios for Self-driving

It is necessary for the development of self-driving to fulfill the requirements for safety, stability, and intelligence, especially in high-speed conditions. Therefore, the detection of pedestrians that may occur in highway scenarios during driving and understanding the meaning of their behaviors in advance are significantly important for the self-driving vehicle to make correct decisions. However, no existing datasets are available for behavior recognition in self-driving scenarios. In order to advance the task of interactive cognition between the vehicle and pedestrians, in this paper, we present a new dataset, called THU-IntrudBehavior, that collects lane intrusion behaviors of pedestrians that can be applied in real world highway scenarios. The dataset contains diverse behaviors of single or multiple pedestrians/cyclists that are simulated in different urban roads under various weather conditions. We describe annotations of each video and report several experimental results of baseline methods on our self-collected dataset. Our THU-IntrudBehavior dataset provides new support for behavior recognition in high-speed conditions for self-driving.