Multi-sensor fusion for Automated Driving: Selecting model and optimizing on Embedded platform

Abstract

Automated Driving requires fusing information from multitude of sensors such as cameras, radars, lidars mounted around car to handle various driving scenarios e.g. highway, parking, urban driving and traffic jam. Fusion also enables better functional safety by handling challenging scenarios such as weather conditions, time of day, occlusion etc. The paper gives an overview of the popular fusion techniques namely Kalman filters and its variation e.g. Extended Kalman filters and Unscented Kalman filters. The paper proposes choice of fusing techniques for given sensor configuration and its model parameters. The second part of paper focuses on efficient solution for series production using embedded platform using Texas Instrument's TDAx Automotive SoC. The performance is benchmarked separately for "predict" and "update" phases on for different sensor modalities. For typical L3/L4 automated driving consisting of multiple cameras, radars and lidars, fusion can supported in real time by single DSP using proposed techniques enabling cost optimized solution.