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Trajectory prediction is, naturally, a key task for vehicle autonomy. Whilethe number of traffic rules is limited, the combinations and uncertaintiesassociated with each agent's behaviour in real-world scenarios are nearlyimpossible to encode. Consequently, there is a growing interest inlearning-based trajectory prediction. The proposed method in this paperpredicts trajectories by considering perception and trajectory prediction as aunified system. In considering them as unified tasks, we show that there is thepotential to improve the performance of perception. To achieve these goals, wepresent BEVSeg2TP - a surround-view camera bird's-eye-view-based joint vehiclesegmentation and ego vehicle trajectory prediction system for autonomousvehicles. The proposed system uses a network trained on multiple camera views.The images are transformed using several deep learning techniques to performsemantic segmentation of objects, including other vehicles, in the scene. Thesegmentation outputs are fused across the camera views to obtain acomprehensive representation of the surrounding vehicles from thebird's-eye-view perspective. The system further predicts the future trajectoryof the ego vehicle using a spatiotemporal probabilistic network (STPN) tooptimize trajectory prediction. This network leverages information fromencoder-decoder transformers and joint vehicle segmentation.

BEVSeg2TP: Surround View Camera Bird's-Eye-View Based Joint Vehicle Segmentation and Ego Vehicle Trajectory Prediction