SocialTraj: 条件付き拡散モデルによる自動運転向け二段階の社会的配慮型軌道予測
SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model
社会価値志向(SVO)をベイズ逆強化学習で推定し、条件付き拡散モデルに組み込むことで、周囲車両の多様な運転行動を社会的に整合した形で予測する手法を提案した。
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著者: Xiao Zhou, Zengqi Peng, Jun Ma
分類: cs.RO
原文アブストラクト
Accurate trajectory prediction of surrounding vehicles (SVs) is crucial for autonomous driving systems to avoid misguided decisions and potential accidents. However, achieving reliable predictions in highly dynamic and complex traffic scenarios remains a significant challenge. One of the key impediments lies in the limited effectiveness of current approaches to capture the multi-modal behaviors of drivers, which leads to predicted trajectories that deviate from actual future motions. To address this issue, we propose SocialTraj, a novel trajectory prediction framework integrating social psychology principles through social value orientation (SVO). By utilizing Bayesian inverse reinforcement learning (IRL) to estimate the SVO of SVs, we obtain the critical social context to infer the future interaction trend. To ensure modal consistency in predicted behaviors, the estimated SVOs of SVs are embedded into a conditional denoising diffusion model that aligns generated trajectories with historical driving styles. Additionally, the planned future trajectory of the ego vehicle (EV) is explicitly incorporated to enhance interaction modeling. Extensive experiments on NGSIM and HighD datasets demonstrate that SocialTraj is capable of adapting to highly dynamic and interactive scenarios while generating socially compliant and behaviorally consistent trajectory predictions, outperforming existing baselines. Ablation studies demonstrate that dynamic SVO estimation and explicit ego-planning components notably improve prediction accuracy and substantially reduce inference time.
