CARIL: 自動運転のための模倣学習における信頼度を考慮した回帰
CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous Driving
回帰ヘッドと分類ヘッドを組み合わせた二頭型ネットワークで、低信頼時の補正機構により自動運転の模倣学習の安定性と精度を向上させた研究。
著者: Elahe Delavari, Aws Khalil, Jaerock Kwon
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
End-to-end vision-based imitation learning has demonstrated promising results in autonomous driving by learning control commands directly from expert demonstrations. However, traditional approaches rely on either regressionbased models, which provide precise control but lack confidence estimation, or classification-based models, which offer confidence scores but suffer from reduced precision due to discretization. This limitation makes it challenging to quantify the reliability of predicted actions and apply corrections when necessary. In this work, we introduce a dual-head neural network architecture that integrates both regression and classification heads to improve decision reliability in imitation learning. The regression head predicts continuous driving actions, while the classification head estimates confidence, enabling a correction mechanism that adjusts actions in low-confidence scenarios, enhancing driving stability. We evaluate our approach in a closed-loop setting within the CARLA simulator, demonstrating its ability to detect uncertain actions, estimate confidence, and apply real-time corrections. Experimental results show that our method reduces lane deviation and improves trajectory accuracy by up to 50%, outperforming conventional regression-only models. These findings highlight the potential of classification-guided confidence estimation in enhancing the robustness of vision-based imitation learning for autonomous driving. The source code is available at https://github.com/ElaheDlv/Confidence_Aware_IL.
