Authors: Xiao Wang, Ke Tang, Xingyuan Dai, Jintao Xu, Quancheng Du, Rui Ai, Yuxiao Wang, Weihao Gu

Published on: April 18, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2404.11946

Summary

• What is new: The integration of Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) within a trajectory planning framework specifically designed for autonomous vehicles in social driving scenarios.
• Why this is important: Autonomous vehicles face challenges interacting safely with human-driven vehicles due to their unpredictable social driving behaviors.
• What the research proposes: A novel framework called social-suitable and safety-sensitive trajectory planning (S4TP) that leverages social-aware modules for predicting human driving behaviors and assessing risks, leading to safer autonomous driving.
• Results: Comprehensive tests using the SMARTS simulator showed that S4TP achieved a 100% pass rate in complex driving scenarios, outperforming current state-of-the-art methods.

Technical Details

Technological frameworks used: S4TP integrates SATP and SADRF for trajectory planning.

Models used: Uses Transformers for driving scene encoding and trajectory prediction, and an optimization-based approach for motion planning.

Data used: nan

Potential Impact

Automotive and mobility sectors, particularly companies developing autonomous driving technologies, and insurance industries focused on calculating premiums based on driving risks.

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