Government data identifies driver behaviour errors as a factor in 94% of car crashes, and autonomous vehicles (AVs), which avoids risky driver behaviours completely, are expected to reduce the number of road crashes significantly. Thus, one of the central focuses of developing AVs is to ensure safety during navigation. However, in reality, AV safety has been far below its expectation, and so far, no government has allowed for complete autonomous driving without human supervision. This paper proposes a dynamic safe path planning algorithm for AVs with Gaussian process regulated risk map. By reasonably assuming that the output of the object detection and tracking module follows a multi-variate Gaussian distribution, we put forward a safe path planning paradigm with Gaussian process regulated risk map, ensuring safety with high confidence. Both simulation results and in-vehicle tests demonstrate the effectiveness of the proposed algorithm.

We present a real-time vehicle detection and tracking system to accomplish the complex task of driving behavior analysis in urban environments. We propose a robust fusion system that combines a monocular camera and a 2D Lidar. This system takes advantage of three key components: robust vehicle detection using deep learning techniques, high precision range estimation from Lidar, and road context from the prior map knowledge. The camera and Lidar sensor fusion, data association and track management are all performed in the global map coordinate system by taking into account the sensors' characteristics. Lastly, behavior reasoning is performed by examining the tracked vehicle states in the lane coordinate system in which the road context is encoded. We validated our approach by tracking a leading vehicle while it performed usual urban driving behaviors such as lane keeping, stop-and-go at intersections, lane changing, overtaking and turning. The leading vehicle was tracked consistently throughout the 2.3 km route and its behavior was classified reliably.