Affiliation:
1. School of Vehicle Engineering, Xi’an Aeronautical University, Xi’an 710077, Shaanxi, P. R. China
2. Key Laboratory for Automotive Transportation, Safety Enhancement Technology of the Ministry of Communication, Xi’an 710064, Shaanxi, P. R. China
Abstract
To improve the specification and applicability of performance indicators for in-vehicle subtask impact experiments, additional driving performance indicators were added to the international standard test environment, the lane-changing test (LCT). The comprehensive characteristics of multiple indicators were studied using the C4.5 decision tree. The paired memory test was selected as a subtask, and data on driving trajectory, speed, and steering angle were collected and analyzed under three task conditions (baseline, easy task, and difficult task). The results showed that the newly added indicators such as lane offset times, initial lane-changing distance, and correct lane-changing ratio could reflect the new driving characteristics from the aspects of the times of lateral deviation over threshold events, driver response, error proportion, etc. Subjects with different driving states were grouped into the lane-keeping indicator and lane-changing indicator. By using the C4.5 decision tree, classification accuracies of 85.2% and 91.3% were achieved, respectively, which indicated a high accuracy rate of task state discrimination. The overall performance of lane keeping and lane changing was different under the action of the subtasks, especially the lane-changing performance.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software