Collision Prediction using UWB and Inertial Sensing: Experimental Evaluation

Real-time proximity and collision detection via radio frequency (RF) distance measurements has application in smart helmets, drones, autonomous vehicles, and social distancing. In this paper we evaluate ACED, a range-based, infrastructure-free, distributed algorithm that utilizes inter-node range data and intra-node acceleration data to estimate the recent relative positions of each node and to predict impending collisions between any pair of nodes. The framework is tested and validated using experimental data from a testbed of mobile nodes which use ultra-wideband (UWB) ranging and inertial sensing. ACED is shown to outperform two state-of-the-art methods.