Empirical Data-driven Identification of Driving Modes in Autonomous Vehicle Road Testing

Owing to the confidentiality of the driving mode data of automobile enterprises, objectively evaluating the driving performance of autonomous vehicle in road testing (AVRT) is difficult. Therefore, this study proposes an empirical data-driven approach to identify the driving modes of AVRT. First, the data features are selected to establish five supervised machine learning classification models: K-nearest neighbor estimation (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and back propagation (BP) neural network algorithms. Second, the threshold values of driving mode durations are determined through non-parametric rank and significance tests. When the duration was greater than the threshold value, the data was considered to be recorded clearly on the driving modes. If the duration is less than, or equal to, the threshold value, the data was labeled as the unclassified dataset, for which the driving modes need further identifications. Seventy percent of the clearly recorded driving mode data were randomly selected as the supervised classification model training dataset, and the remaining 30% were treated as the test dataset. Finally, the five types of supervised classification models were evaluated using three indicators: correctness, accuracy, and recall. The classification model with the best performance was selected to identify the driving mode of the unclassified dataset. The proposed approach on driving mode identification was verified based on 0.436 million road testing records from urban-road and highway scenarios in the city of Shanghai. The results show that RF supervision model is the most suited for driving mode identification in AVRT. The inaccuracy rates for driving modes in the AVRT data for the urban roads and highways were found to be 42.3% and 39.4%, respectively. The driving ability assessment accuracy rate in AVRT is significantly improved by identifying and repairing the incorrectly recorded driving modes in the empirical data. © 2021, Editorial Department of China Journal of Highway and Transport. All right reserved.