S2O将人类主观驾驶感受转化为客观评估，提升自动驾驶决策评价精度。
S2O: An Integrated Driving Decision-making Performance Evaluation Method Bridging Subjective Feeling to Objective Evaluation
- 构建安全、效率、舒适、能耗四类驾驶因素模型，覆盖主流驾驶维度。
- 通过分段线性拟合与SVM分类器，实现主观评分与客观指标的精准映射。
- 在D2E数据集上误差降低32.55%，支持SUMO平台实时评估，适合算法开发者使用。
自动驾驶决策是智能交通系统的关键模块，如何全面精确地评估驾驶性能是一大挑战。现有评估指标存在偏差，如轨迹偏离度无法反映真实驾驶质量，而现有客观体验模型仅考虑有限因素且整合机制依赖经验。本文提出S2O，一种融合主观感受与客观评价的集成决策评估方法。首先建立安全、时间效率、舒适性、能量效率四类驾驶因素的改进模型；其次基于人类评分分布规律，设计分段线性拟合模型结合互补SVM分类器，将主观评分映射为客观因子表达。在包含约1000个驾驶场景和4万条人工评分的D2E数据集上实验表明，S2O在百分制下平均绝对误差为4.58，相比基线误差降低32.55%。在SUMO平台上的实现验证了其在线评估的实时性，对三种自动驾驶规划算法的性能评估验证了方法可行性。
原文摘要 · Abstract (English)
Autonomous driving decision-making is one of the critical modules towards intelligent transportation systems, and how to evaluate the driving performance comprehensively and precisely is a crucial challenge. A biased evaluation misleads and hinders decision-making modification and development. Current planning evaluation metrics include deviation from the real driver trajectory and objective driving experience indicators. The former category does not necessarily indicate good driving performance since human drivers also make errors and has been proven to be ineffective in interactive close-loop systems. On the other hand, existing objective driving experience models only consider limited factors, lacking comprehensiveness. And the integration mechanism of various factors relies on intuitive experience, lacking precision. In this research, we propose S2O, a novel integrated decision-making evaluation method bridging subjective human feeling to objective evaluation. First, modified fundamental models of four kinds of driving factors which are safety, time efficiency, comfort, and energy efficiency are established to cover common driving factors. Then based on the analysis of human rating distribution regularity, a segmental linear fitting model in conjunction with a complementary SVM segment classifier is designed to express human's subjective rating by objective driving factor terms. Experiments are conducted on the D2E dataset, which includes approximately 1,000 driving cases and 40,000 human rating scores. Results show that S2O achieves a mean absolute error of 4.58 to ground truth under a percentage scale. Compared with baselines, the evaluation error is reduced by 32.55%. Implementation on the SUMO platform proves the real-time efficiency of online evaluation, and validation on performance evaluation of three autonomous driving planning algorithms proves the feasibility.
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