Authors:
Sushil Sharma
1
;
2
;
Arindam Das
1
;
Ganesh Sistu
1
;
Mark Halton
1
and
Ciarán Eising
1
;
2
Affiliations:
1
Department of Electronic & Computer Engineering, University of Limerick, Ireland
;
2
SFI CRT Foundations in Data Science, University of Limerick, Ireland
Keyword(s):
Surrounded-View Camera, Encoder-Decoder Transformer, Segmentation, Trajectory Prediction.
Abstract:
Trajectory prediction is, naturally, a key task for vehicle autonomy. While the number of traffic rules is limited, the combinations and uncertainties associated with each agent’s behaviour in real-world scenarios are nearly impossible to encode. Consequently, there is a growing interest in learning-based trajectory prediction. The proposed method in this paper predicts trajectories by considering perception and trajectory prediction as a unified system. In considering them as unified tasks, we show that there is the potential to improve the performance of perception. To achieve these goals, we present BEVSeg2TP - a surround-view camera bird’s-eye-view-based joint vehicle segmentation and ego vehicle trajectory prediction system for autonomous vehicles. The proposed system uses a network trained on multiple camera views. The images are transformed using several deep learning techniques to perform semantic segmentation of objects, including other vehicles, in the scene. The segmentati
on outputs are fused across the camera views to obtain a comprehensive representation of the surrounding vehicles from the bird’s-eye-view perspective. The system further predicts the future trajectory of the ego vehicle using a spatiotemporal probabilistic network (STPN) to optimize trajectory prediction. This network leverages information from encoder-decoder transformers and joint vehicle segmentation. The predicted trajectories are projected back to the ego vehicle’s bird’s-eye-view perspective to provide a holistic understanding of the surrounding traffic dynamics, thus achieving safe and effective driving for vehicle autonomy. The present study suggests that transformer-based models that use cross-attention information can improve the accuracy of trajectory prediction for autonomous driving perception systems. Our proposed method outperforms existing state-of-the-art approaches on the publicly available nuScenes dataset. This link is to be followed for the source code: https://github.com/sharmasushil/BEVSeg2TP/.
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