Real-Time Implementation and Validation for Automated Path Following Lateral Control Using Hardware-in-the-Loop (HIL) Simulation 2017-01-1683
Software for autonomous vehicles is highly complex and requires vast amount of vehicle testing to achieve a certain level of confidence in safety, quality and reliability. According to the RAND Corporation, a 100 vehicle fleet running 24 hours a day 365 days a year at a speed of 40 km/hr, would require 17 billion driven kilometers of testing and take 518 years to fully validate the software with 95% confidence such that its failure rate would be 20% better than the current human driver fatality rate . In order to reduce cost and time to accelerate autonomous software development, Hardware-in-the-Loop (HIL) simulation is used to supplement vehicle testing. For autonomous vehicles, path following controls are an integral part for achieving lateral control. Combining the aforementioned concepts, this paper focuses on a real-time implementation of a path-following lateral controller, developed by Freund and Mayr . The controller is implemented on a powertrain subsystem HIL simulation bench to enable lateral control of the longitudinal controlled HIL setup for automated driving applications. 2017 Ford Fusion Hybrid powertrain controllers and actuators were used as the hardware platform for the powertrain subsystem. The simulation of other subsystem plants and controllers was achieved by using a real-time CarSim-Simulink co-simulation environment representative of the 2017 Ford Fusion Hybrid through a dSPACE HIL simulator.
The objectives of this research were three-fold. The first objective was to implement a real-time version of the path-following lateral controller to add lateral capability to a powertrain-based longitudinal controlled HIL setup. The second objective was to validate the path-following capability of the lateral controller. Lastly, the third objective was to quantitatively understand the real-time behavior and sensitivity of the lateral controller using simulations over varying vehicle inertial and environmental conditions such as speed, payload mass, payload position, surface type/friction, rapid acceleration/deceleration, and crosswinds.