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Radar sensors have become an important part of the perception sensor suitedue to their long range and their ability to work in adverse weatherconditions. However, several shortcomings such as large amounts of noise andextreme sparsity of the point cloud result in them not being used to their fullpotential. In this paper, we present a novel Recursive Least Squares (RLS)based approach to estimate the instantaneous velocity of dynamic objects inreal-time that is capable of handling large amounts of noise in the input datastream. We also present an end-to-end pipeline to track extended objects inreal-time that uses the computed velocity estimates for data association andtrack initialisation. The approaches are evaluated using several real-worldinspired driving scenarios that test the limits of these algorithms. It is alsoexperimentally proven that our approaches run in real-time with frame executiontime not exceeding 30 ms even in dense traffic scenarios, thus allowing fortheir direct implementation on autonomous vehicles.

An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking