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Batch human-like trajectory generation for multi-motion-state NPC-vehicles in autonomous driving virtual simulation testing

Author

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  • Wei, Cheng
  • Hui, Fei
  • Khattak, Asad J.
  • Zhao, Xiangmo
  • Jin, Shaojie

Abstract

Non-player character vehicles (NPC-Vs) denote crucial components of autonomous driving systems (ADSs) and autonomous driving assistance algorithms (ADAAs) when conducting virtual simulation testing (VST). Human-like behaviors and trajectories of NPC-Vs could provide information on complex background traffic flow to the tested ADS and ADAA, thus ensuring rigorous tests on the reliability and stability of the ADS and ADAA. However, a VST based on data injection faces the problems of a small amount of data and difficulty in extracting critical scenarios and methods’ transplantation and reuse. To address these problems, this study takes intersection as a research scenario and proposes a probability-limited parameter combination method and a learning-based batch human-like trajectory generation model to generate different human-like trajectories according to different motion states of vehicles. First, an effective IM-sampling algorithm, which samples trajectory data and obtains equal-number-coordinate trajectories as trajectory generation model labels, is proposed. Second, dependency probabilities between different vehicle kinematic parameters (VKPs) are calculated to form a probability-limited generation tree, which generates different VKP combinations representing different vehicle motion states that are used as trajectory generation model inputs. Finally, a learning-based batch trajectory generation model is developed. After model training and testing, the generated VKP combinations are used for trajectory generation, and the generated trajectories are subjected to the human-like degree analysis considering multiple metrics. The experimental results show that the proposed model is capable of generating more complex human-like trajectories and behaviors than real trajectories in batch. The proposed model could be used to generate complex and human-like NPC-V trajectories for the autonomous driving VST and thus accelerate the autonomous driving VST.

Suggested Citation

  • Wei, Cheng & Hui, Fei & Khattak, Asad J. & Zhao, Xiangmo & Jin, Shaojie, 2023. "Batch human-like trajectory generation for multi-motion-state NPC-vehicles in autonomous driving virtual simulation testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    • Handle: RePEc:eee:phsmap:v:616:y:2023:i:c:s0378437123001838
    DOI: 10.1016/j.physa.2023.128628
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    References listed on IDEAS

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    1. Hui, Fei & Wei, Cheng & ShangGuan, Wei & Ando, Ryosuke & Fang, Shan, 2022. "Deep encoder–decoder-NN: A deep learning-based autonomous vehicle trajectory prediction and correction model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    2. Shuo Feng & Xintao Yan & Haowei Sun & Yiheng Feng & Henry X. Liu, 2021. "Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Kalra, Nidhi & Paddock, Susan M., 2016. "Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 182-193.
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