脳に着想を得た人間らしい自動運転のモデリングと意思決定：混在交通環境への適用
Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment
実世界のINTERACTIONデータセットから運転の攻撃性をファジィ推論で推定し、脳の情動学習回路と二点予視モデルを統合した人間らしい運転モデルを構築。車線変更では安全性と効率を考慮したコスト関数と動的ゲームで自動車と人間ドライバーの相互作用をモデル化し、人工ポテンシャル場で衝突リスクを評価、ドライビングシミュレータの人間参加型実験で有効性を確認した。
著者: Peng Hang, Yiran Zhang, Chen Lv
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In this paper, a human-like driving framework is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the transportation ecology of human driving and eliminate the misunderstanding and incompatibility of human drivers to autonomous driving. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. Then, a human-like driving model, which integrates the brain emotional learning circuit model (BELCM) with the two-point preview model, is designed. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function and multi-constraint, the dynamic game algorithm is applied to modelling the interaction and decision making between AV and human driver. Additionally, to guarantee the lane-change safety of AVs, an artificial potential field model is built for collision risk assessment. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments on a driving simulator, and the results demonstrated the feasibility and effectiveness of the proposed method.
