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Learning Latent Traits for Simulated Cooperative Driving Tasks

arXiv 2207.09619 ↗First posted 20 Jul 2022cs.HC cs.AI cs.ROcited by 0

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Abstract (arXiv)

To construct effective teaming strategies between humans and AI systems in complex, risky situations requires an understanding of individual preferences and behaviors of humans. Previously this problem has been treated in case-specific or data-agnostic ways. In this paper, we build a framework capable of capturing a compact latent representation of the human in terms of their behavior and preferences based on data from a simulated population of drivers. Our framework leverages, to the extent available, knowledge of individual preferences and types from samples within the population to deploy interaction policies appropriate for specific drivers. We then build a lightweight simulation environment, HMIway-env, for modelling one form of distracted driving behavior, and use it to generate data for different driver types and train intervention policies. We finally use this environment to quantify both the ability to discriminate drivers and the effectiveness of intervention policies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 20 Jul 2022.

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18 of 39 references.

References · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  11. 2021Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planningcited by 43
  12. 2021AGENT: A Benchmark for Core Psychological Reasoningcited by 42
6 more
  1. 2019DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Samplingcited by 40
  2. 2020Improving Dialog Systems for Negotiation with Personality Modelingcited by 38
  3. 2020Residual Policy Learning for Shared Autonomycited by 26
  4. 2017Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behaviorcited by 16
  5. 2020StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectoriescited by 11
  6. 2022Reinforcement Learning for Shared Autonomy Drone Landingscited by 3

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