CoPreMo: A Collaborative Predictive Model in Time Series and Its Application to Radar Target Tracking for ADAS/AD Vehicles

Automotivedriving assistant systems (ADAS) and Automated driving (AD) technologies are commonly employed in the control of unmanned vehicles along a path. However, like many other technologies, they come with risks, including potential misdirection and collisions with obstacles along the vehicle's route. To mitigate these risks, various tracking systems, including radar tracking systems, are employed to detect and monitor targets along the trajectories of ADAS/AD vehicles. Nevertheless, the effectiveness of these tracking operations is crucial in assessing the reliability of both tracking systems and ADAS/AD technologies, especially at the edge-computing level. In this study, we introduce a tracking technique using a collaborative predictive model in a time series, named CoPreMo, aimed at enhancing the reliability of radar system tracking operations. We conducted three experiments with this model on a simulated radar system to track a target's range at varying speeds across three ADAS/AD scenarios. The experiments yielded range tracking errors of 0.21 m, 0.26 m, and 0.32 m, outperforming the baseline models.