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TrafficBots V1.5: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding

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arxiv 2406.10898 v1 pith:CNFP2KTR submitted 2024-06-16 cs.RO cs.CV

Zhejun Zhang , Christos Sakaridis , Luc Van Gool This is my paper · ORCID
classification cs.ROcs.CV
keywords trafficbotsagentsbaselineencodingperformanceposerelativesimulation
verification ladder T0 review T1 audit T2 compute T3 formal

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In this technical report we present TrafficBots V1.5, a baseline method for the closed-loop simulation of traffic agents. TrafficBots V1.5 achieves baseline-level performance and a 3rd place ranking in the Waymo Open Sim Agents Challenge (WOSAC) 2024. It is a simple baseline that combines TrafficBots, a CVAE-based multi-agent policy conditioned on each agent's individual destination and personality, and HPTR, the heterogeneous polyline transformer with relative pose encoding. To improve the performance on the WOSAC leaderboard, we apply scheduled teacher-forcing at the training time and we filter the sampled scenarios at the inference time. The code is available at https://github.com/zhejz/TrafficBotsV1.5.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation

    cs.CV 2025-06 conditional novelty 7.0 of 10

    InfGen is a single next-token-prediction transformer that interleaves motion simulation with scene generation, keeping traffic realistic over 30-second rollouts better than motion-only simulators.

  2. Highly Accurate and Diverse Traffic Data: The DeepScenario Open 3D Dataset

    cs.CV 2025-04 conditional novelty 6.0 of 10

    DSC3D is a new open 3D trajectory dataset from drone footage with over 175,000 annotated 6DoF trajectories across five locations and 14 traffic participant classes.

  3. On Learning Closed-Loop Probabilistic Multi-Agent Simulator

    cs.RO 2025-08 conditional novelty 4.0 of 10

    NIVA, a hierarchical Bayesian multi-agent traffic simulator, disentangles intention and driving-style latents and matches state-of-the-art results on the Waymo dataset.

  4. Generative AI for Autonomous Driving: Frontiers and Opportunities

    cs.CV 2025-05 accept novelty 2.0 of 10

    A comprehensive, structured survey of generative AI for autonomous driving, covering model families, sensor modalities, real-world applications, and open research challenges.

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