A path fitting accuracy improvement and evaluation method based on minimum toll and multi-dimensional anomaly detection engine

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

<title>Abstract</title> Aiming at the problems of inaccurate path fitting and “same road, different tolls” caused by missing or abnormal gantry data in expressway network tolling, this paper proposes an innovative two-stage path fitting framework. The first stage (baseline fitting) rapidly generates spatiotemporally reasonable paths based on the whole-network topology. The second stage (optimized fitting) deeply integrates the business principle of “minimum toll” with a parallelized multi-dimensional anomaly detection engine, which systematically defines six types of real-world anomalies and provides their mathematical detection models. In addition, this paper is the first to propose a dual-indicator quality evaluation model, providing a quantitative basis for the reliability of fitting results. Experiments based on real transaction data show that, compared with traditional shortest-path fitting and baseline methods, the proposed framework improves path fitting accuracy by approximately 2.1% and reduces the billing deviation rate by nearly 70%. The confidence indicator exhibits a strong negative correlation with actual fitting error (Pearson coefficient = -0.87), verifying the effectiveness of the evaluation model. Finally, case scenarios demonstrate the practical application of the two-stage framework and evaluation model. This work provides a reliable theoretical and practical solution for realizing accurate and fair expressway network tolling.

Links and sources

Comments