Deep Stochastic Radar Models

Understand

Accurate simulation and validation of advanced driver assistance systems requires accurate sensor models.

• Modeling automotive radar is complicated by effects such as multipath reflections, interference, reflective surfaces, discrete cells, and attenuation.
• Detailed radar simulations based on physical principles exist but are computationally intractable for realistic automotive scenes.
• This paper describes a methodology for the construction of stochastic automotive radar models based on deep learning with adversarial loss connected to real-world data.