Readiness Evaluation on Freeways in Plain Regions for Automated Vehicles Based on Lane Detection Performance

To evaluate the readiness of freeways in plain regions for automated vehicles (AVs) from the perspective of lane detection performance, field tests were conducted on the Beijing-Shanghai Freeway and Shenyang-Haikou Freeway in Shanghai using a test vehicle equipped with the Tongji University Road and Traffic Holographic Data Acquisition System. By considering scenarios in which the test vehicle and surrounding vehicles in adjacent lanes could potentially collide laterally as safety-critical conditions, the upper and lower thresholds for lane width detection were calculated, and lane-detection failure events were extracted. The lane-detection failure type was used as the label. Five feature types, namely road geometric design, road section, road marking, vehicle operation, and environment, were considered as input features. Using an XGBoost ensemble learning model, the relationship between the lane detection failure type and the features was established. As a post hoc interpretation technique, the SHapley Additive exPlanations (SHAP) was used to analyze the feature importance and the impacts of individual and interaction features on failure types. The results show that features including speed, segment type, leading-truck distance, lane location, special marking type, average curvature, rate of change of vertical curve, marking condition, and longitudinal line type affect failure probabilities, with feature importance decreasing in order. Specifically, the failure probability increases when the average curvature is smaller than 0.4 km-1, the change rates of crest and sag curves exceed 10%·km-1 and 30%·km-1, respectively, vehicles are on the mainline at entrance or exit or in the right-most lane, lane markings are connected with acceleration or deceleration tapers, special markings are present or poorly maintained, or the leading truck distance ranges from 0 to 55 m. Moreover, approaches such as continuous connection of markings at acceleration or deceleration taper extensions, better maintenance of worn-out and unerased markings, and the use of solid lines on important lanes can reduce the probability of failure. These findings can be applied in conducting readiness evaluations on freeways from the perspective of lane-detection performance for AVs, providing quantitative references for traffic departments to manage AVs' operation design domain, and guiding lidar performance optimization for sensors and automobile manufacturers. © 2024 Chang'an University. All rights reserved.