Lightweight multi task learning for joint birds eye view instance mapping and trajectory prediction
TL;DR
BEV-IMTP is presented, a lightweight instance mapping and trajectory prediction network designed for Bird's-Eye-View representations that achieves a 4.6% improvement in semantic map mIoU and a 1.8% gain in instance motion mIoU compared to state-of-the-art methods.
Abstract
Trajectory prediction is a critical component of autonomous driving systems, enabling vehicles to anticipate the motion of surrounding agents and make safe, informed navigation decisions. This paper presents BEV-IMTP, a lightweight instance mapping and trajectory prediction network designed for Bird’s-Eye-View representations. The proposed framework adopts a streamlined architecture with customized core layers and a minimal-parameter backbone, significantly reducing computational overhead while making it a promising candidate for latency-sensitive autonomous driving applications. BEV-IMTP achieves a 4.6% improvement in semantic map mIoU and a 1.8% gain in instance motion mIoU compared to state-of-the-art methods. On the nuScenes benchmark, the proposed model attains an overall semantic map mIoU of 62.1%, with strong class-wise performance of 63.4% for divider, 58.3% for pedestrian crossing, and 64.6% for boundary regions. Despite its high accuracy, BEV-IMTP remains computationally efficient, requiring only 34.5 million parameters and 88 GFLOPs, and supports inference at 3.1 FPS on a single Tesla V100 GPU. The model is trained on the nuScenes dataset and further evaluated on the Lyft dataset, demonstrating robust generalization across datasets captured using cameras with varying configurations. Extensive experiments on publicly available trajectory prediction benchmarks validated the effectiveness and efficiency of the proposed BEV-IMTP framework.