条件付き複数軌道合成による安全クリティカルな運転シナリオの生成
CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios
道路地図を条件とした生成的モデルで安全運転と衝突運転の潜在空間を橋渡しし、ニアミス軌道を合成して自動運転の予測精度と危険対応能力を向上させるフレームワークを提案した。
著者: Wenhao Ding, Mengdi Xu, Ding Zhao
分類: cs.LG, cs.CV, cs.RO, eess.IV, stat.ML
原文アブストラクト
Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss scenarios that rarely show up in off-the-shelf datasets is a vital part of the evaluation. As a remedy, we propose a near-miss data synthesizing framework based on Variational Bayesian methods and term it as Conditional Multiple Trajectory Synthesizer (CMTS). We leverage a generative model conditioned on road maps to bridge safe and collision driving data by representing their distribution in the latent space. By sampling from the near-miss distribution, we can synthesize safety-critical data crucial for understanding traffic scenarios but not shown in neither the original dataset nor the collision dataset. Our experimental results demonstrate that the augmented dataset covers more kinds of driving scenarios, especially the near-miss ones, which help improve the trajectory prediction accuracy and the capability of dealing with risky driving scenarios.
