Deep Learned Multi-Modal Traffic Agent Predictions for Truck Platooning Cut-Ins

Abstract

Recent advances in Driver-Assisted Truck Platooning (DATP) have shown success in linking multiple trucks in leader-follower platoons using Cooperative Adaptive Cruise Control (CACC). Such set ups allow for closer spacing between trucks which leads to fuel savings. Given that frontal collisions are the most common type of highway accident for heavy trucks, one key issue to truck platooning is handling situations in which vehicles cut-in between platooning trucks. Having more accurate and quicker predictions would improve the safety and efficiency of truck platooning by allowing the control system to react to the intruder sooner and allow for proper spacing before the cutin occurs. Moreover, reduction in false-positives could prevent the CACC from reacting to cut-in vehicles too early, leading to increased benefit from DATP. In this paper, we implement a deep neural network that generates multimodal predictions of traffic agents around a truck platoon. The method uses Long Short-Term Memory networks in an ensemble architecture to predict possible future positions with attached probabilities of vehicles passing by a truck platoon for 5 second horizons. The network performance is compared to a baseline of common state-based predictors including the Constant Velocity Predictor, the Constant Acceleration Predictor, and the Constant Steer Predictor.