Robust Validation Platform of Autonomous Capability for Commercial Vehicles 2019-01-0686

Global deployment of autonomous capability for commercial vehicles is a big challenge. In order to improve the robustness of autonomous approach under different traffic scenarios, environments, road conditions, and driver behaviors, a combined approach of virtual simulation, vehicle-in-the-loop (VIL) testing, proving ground testing, and final field testing have been established for algorithms validation. During the validation platform setup, different platforms for different functionalities have been studied, including open source virtual testing environment (CARLA, AirSim), and commercial one (IPG). We also cooperate with MCity to do proving ground validation. In virtual testing, the functionality of sensors (camera, radar, Lidar, GPS, IMU) and vehicle dynamic models can be applied in the virtual environment. In VIL testing, real world and virtual test will be connected for different validation purposes. The proving ground testing will be performed in real environment with rich scenarios and high safety. Several challenges have been overcome during implementation, including data transmission, computing time, sensor system consistency, vehicle dynamic model consistency and etc. In this paper, a robust autonomous driving validation platform, including perception, planning and control algorithm, will be introduced in different virtual and physical validation approaches. Several test case studies for algorithm testing will be discussed. And conclusions will be made on the established validation platforms and next steps for the development and improvement of commercial vehicle’s autonomous capability.