Handoff of Advanced Driver Assistance Systems (ADAS) using a Driver-in-the-Loop Simulator and Model Predictive Control (MPC)

The objective of this work is to benchmark Advanced Driver Assistance Systems (ADAS) using a limited motion Driver-in-the-Loop (DiL) Simulator and Model Predictive Control (MPC). These ADAS features include Adaptive Cruise Control (ACC), Lane Keeping Assist (LKA), Forward Collision Warning (FCW), and Automatic Emergency (AEB). The handoff of these features is at the discretion of the driver but maintains the operational design domain. Simplified internal models for the ACC MPC and LKA MPC are presented, so the MPC toolbox CasADi could be used as Software-in-the-Loop (SiL). Both MPC’s leverage adaptive linear techniques alleviating the inherent nonlinearities. SiL ensures robust, real-time execution of the features integrating with the simulator.Regulators and automotive manufacturers are tasked with eliminating automotive deaths and injuries. Of active safety tools at their disposal, ADAS features provide a promising ability to aiding this cause. Driving simulators are becoming an important development tool for active safety systems, automated driving features, and vehicle dynamics development. This driving simulator couples SCANeR Studio®, CarSim®, and MATLAB/Simulink®. Refined and custom cues give the driver a sense of the virtual world providing the immersion. Offline verification using the testbed and sample results using the driving simulator shows the efficacy of prototyping and evaluating ADAS features using the simulator. Combining these elements allows for both quantitative and qualitative assessment of the systems’ functionality, performance, and safety assurance.