Resilient Multi-range Radar Detection System for Autonomous Vehicles: A New Statistical Method

Abstract

Critical issues with current detection systems are their susceptibility to adverse weather conditions and constraint on the vertical field view of the radars limiting the ability of such systems to accurately detect the height of the targets. In this paper, a novel multi-range radar (MRR) arrangement (i.e. triple: long-range, medium-range, and short-range radars) based on the sensor fusion technique is investigated that can detect objects of different sizes in a level 2 advanced driver-assistance system. To improve the accuracy of the detection system, the resilience of the MRR approach is investigated using the Monte Carlo (MC) method for the first time. By adopting MC framework, this study shows that only a handful of fine-scaled computations are required to accurately predict statistics of the radar detection failure, compared to many expensive trials. The results presented huge computational gains for such a complex problem. The MRR approach improved the detection reliability with an increased mean detection distance (4.9% over medium range and 13% over long range radar) and reduced standard deviation over existing methods (30% over medium range and 15% over long-range radar). This will help establishing a new path toward faster and cheaper development of modern vehicle detection systems.