GCL-BEV: Motion-Aware Temporal Compensation for Multi-Camera Vehicle Sensing Under Aggressive Ego-Motion

Abstract

Multi-camera vehicle sensing provides a cost-effective solution for 3D environmental perception in intelligent vehicles. A practical challenge is that temporal fusion becomes unreliable when the ego-vehicle undergoes aggressive motion. Historical camera features are commonly aligned by rigid ego-pose warping, but residual motion-induced displacement can still degrade object localization, heading estimation, and velocity sensing. This paper presents GCL-BEV, a motion-aware temporal compensation framework for multi-camera vehicle sensor systems. The proposed framework uses synchronized surround-view cameras and ego-motion measurements as coupled sensing inputs. First, a Geometric-Aware Feature Enhancement (GAFE) module converts ego-motion priors into motion-conditioned BEV sampling offsets, allowing the visual sensing representation to compensate for local temporal misalignment before fusion. Second, a View-Consistency Learning (VCL) objective imposes a training-time equivariance constraint so that the sensor representation remains consistent under planar viewpoint perturbations. Across 10 random seeds on nuScenes, GCL-BEV achieves 57.80% ± 0.15 NDS and 46.22% ± 0.16 mAP with a ResNet-101 backbone. Compared with BEVDet4D, it reduces the mean Average Orientation Error by 5.4% and shows smaller degradation from steady driving to high-turn scenarios, indicating improved robustness for dynamic vehicle sensing.