Automated Complex Urban Driving based on Enhanced Environment Representation with GPS/map, Radar, Lidar and Vision

This paper describes a fully automated driving algorithm on complex urban roads with guaranteed safety. A serial-production sensor setup is modified as follows: Radar, LiDAR, vision, and GPS/map. The proposed algorithm consists of the following three steps: an environment representation, a safety driving envelope decision, and a motion optimization. An environment representation system consists of three main modules: object classification, vehicle/non-vehicle tracking and map/lane based localization. A motion planning modules derives an optimal motion as a function of time, from the environment representation results. A safety envelope decision module determines the complete driving corridor that leads to the destination while assigning all objects to either the left or right corridor bound. In the case of moving objects such as other traffic participants, their behaviors are anticipated in the near future. A motion optimization module uses the safety envelop as a constraint and computes a trajectory that the vehicle stays in its bounds. The vehicle control module feeds back the pose estimate of the localization module to guide the vehicle along the planned trajectory. The effectiveness of the proposed automated driving algorithm is evaluated via vehicle tests. Test results show the robust performance on an inner-city street scenario.