Deep Stochastic Radar Models

摘要

Accurate simulation and validation of advanced driver assistance systemsrequires accurate sensor models. Modeling automotive radar is complicated byeffects such as multipath reflections, interference, reflective surfaces,discrete cells, and attenuation. Detailed radar simulations based on physicalprinciples exist but are computationally intractable for realistic automotivescenes. This paper describes a methodology for the construction of stochasticautomotive radar models based on deep learning with adversarial loss connectedto real-world data. The resulting model exhibits fundamental radar effectswhile remaining real-time capable.