Multiple Scale Aggregation with Patch Multiplexing for High-Speed Inter-Vehicle Distance Estimation

Abstract

We propose an accurate and robust inter-vehicle distance estimation method using highspeed stereo vision. The framework involves two phases: a tracking phase, wherein a preceding vehicle is accurately and stably tracked by a tracking algorithm optimized for stereo high-speed vision, and a distance estimation phase, wherein the inter-vehicle distance is estimated via a highly accurate scale estimation and aggregation method for multiple scale-based distance estimations to ensure that it is more accurate and robust without introducing a delay. Further, we propose patch multiplexing to realize accurate and efficient aggregation even in situations where the scale changes rapidly (e.g., emergency braking). Through comparative analysis using three real-world scenarios, we verify that the accuracy of inter-vehicle distance estimation using our approach is comparable to that of laser rangefinders. We also demonstrate that differential quantities, such as velocity and acceleration, could be accurately estimated using an adaptive Kalman filter. Our results will help develop safe and accurate truck platooning and adaptive cruise control systems.