Deep Stochastic Radar Models

TL;DR

This paper introduces a deep learning-based stochastic radar model that captures key automotive radar effects and operates in real-time, facilitating accurate simulation and validation of driver assistance systems.

Contribution

It presents a novel deep learning approach with adversarial training to model complex radar phenomena efficiently for realistic automotive scenarios.

Findings

Model captures multipath reflections and interference.

Operates in real-time for practical simulation.

Aligns closely with real-world radar data.

Abstract

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. The resulting model exhibits fundamental radar effects while remaining real-time capable.