Improving Ego-Velocity Estimation of Low-Cost Doppler Radars for Vehicles

A low-cost automotive radar is often used in autonomous driving and advanced driver-assistance systems. However, the low cost radar often assumes all detected objects to be on the ground plane when estimating radar/vehicle ego-velocity, but when there are elevated background objects present, such as buildings and tall trees, the ego-velocity estimation tends to be biased. Here we analyze the source of estimation error and develop a new algorithm to recognize three types of object reflections using the discrepancy between the estimated ego-velocity and the measured Doppler velocity. We propose an elevation and background aware cost (EBAC) function to formulate an optimization framework which can distinguish the object types to improve ego-velocity estimation. We combine a robust estimation method with the optimization framework to handle outliers in radar readings. We have implemented the algorithm and tested it in both simulation and physical experiments using our autonomous vehicle. The results show that our estimation method significantly reduces ego-velocity estimation error while maintaining a smaller error variance without losing robustness. More specifically, it reduces the ego-velocity estimation error by 49% in the most common driving scenario.