Machine Learning-Based Radar Perception for Autonomous Vehicles Using Full Physics Simulation

Safety critical systems in Advanced Driver Assistance Systems (ADAS) depend on multiple sensors to perceive the environment in which they operate. Radar sensors provide many advantages and complementary capabilities to other available sensors but are not without their own shortcomings. Performance of radar perception algorithms still pose many challenges, one of which is in object detection and classification. In order to increase redundancy in ADAS, the ability for a radar system to detect and classify objects independent of other sensors is desirable. In this paper, an investigation of a machine learning based radar perception algorithm for object detection is implemented, along with a novel, automated workflow for generating large-scale virtual datasets used for training and testing. Physics-based electromagnetic simulation of a complex scattering environment is used to create the virtual dataset. Objects are classified and localized within Doppler-Range results using a single channel 77 GHz FMCW radar system. Utilizing a fully convolutional network, the radar perception model is trained and tested. The training is performed using a wide range of environments and traffic scenarios. Model inference is tested on completely new environments and traffic scenarios. These simulated radar returns are highly scalable and offer an efficient method for dataset generation. These virtual datasets facilitate a simple method of introducing variability in training data, corner case evaluation and root cause analysis, amongst other advantages.