TY - GEN
T1 - SSN
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
AU - Li, Haichuan
AU - Zhou, Liguo
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Autonomous driving has been an active area of research and development, with various strategies being explored for decision-making in autonomous vehicles. Rule-based systems, decision trees, Markov decision processes, and Bayesian networks have been some of the popular methods used to tackle the complexities of traffic conditions and avoid collisions. However, with the emergence of deep learning, many researchers have turned towards Bird's-Eye View based methods to improve the precision of trajectory prediction. Despite the promising results achieved by some CNN-based methods, the failure to establish correlations between sequential images often leads to more collisions. In this paper, we propose an attention-based method that overcomes the limitation by establishing feature correlations between regions in Bird's-Eye View images using variants of multi-head attention. Our method combines the advantages of CNN with different kernel sizes in capturing regional features with multi-head self-attention structure to enhance the relationship between different local areas. Our method takes 'Bird's Eye View' graphs generated from camera and LiDAR sensors as input, and simulates the position (x, y) and head offset angle (Yaw) to generate future trajectories. Each trajectory consists of 12 way-points and each point contains the above position and yaw information. Experiment results demonstrate that our proposed method outperforms existing vision-based strategies, achieving an average of only 12.4 collisions per 1000 miles of driving distance on the L5kit test set. This significantly improves the success rate of collision avoidance and provides a potential solution for autonomous driving. We have uploaded the GitHub link11Github link: https://github.com/HaynesLi/SSN of this algorithm.
AB - Autonomous driving has been an active area of research and development, with various strategies being explored for decision-making in autonomous vehicles. Rule-based systems, decision trees, Markov decision processes, and Bayesian networks have been some of the popular methods used to tackle the complexities of traffic conditions and avoid collisions. However, with the emergence of deep learning, many researchers have turned towards Bird's-Eye View based methods to improve the precision of trajectory prediction. Despite the promising results achieved by some CNN-based methods, the failure to establish correlations between sequential images often leads to more collisions. In this paper, we propose an attention-based method that overcomes the limitation by establishing feature correlations between regions in Bird's-Eye View images using variants of multi-head attention. Our method combines the advantages of CNN with different kernel sizes in capturing regional features with multi-head self-attention structure to enhance the relationship between different local areas. Our method takes 'Bird's Eye View' graphs generated from camera and LiDAR sensors as input, and simulates the position (x, y) and head offset angle (Yaw) to generate future trajectories. Each trajectory consists of 12 way-points and each point contains the above position and yaw information. Experiment results demonstrate that our proposed method outperforms existing vision-based strategies, achieving an average of only 12.4 collisions per 1000 miles of driving distance on the L5kit test set. This significantly improves the success rate of collision avoidance and provides a potential solution for autonomous driving. We have uploaded the GitHub link11Github link: https://github.com/HaynesLi/SSN of this algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85186505383&partnerID=8YFLogxK
U2 - 10.1109/ITSC57777.2023.10421881
DO - 10.1109/ITSC57777.2023.10421881
M3 - Conference contribution
AN - SCOPUS:85186505383
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 635
EP - 641
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2023 through 28 September 2023
ER -