A matched case-control analysis of autonomous vs human-driven vehicle accidents

Abstract

Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles' levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous' versus Human-Driven Vehicles' accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements.

Fig. 1. Number of accidents related to the studies of vehicles with driving automation (ADS/ADAS).

The blue line shows the number of ADS data samples, and the orange line shows the number of ADAS data samples used in related studies.

Fig. 2. Distribution of the factors influencing accidents of various vehicle types.

a HDV accidents with a sample of 35,133. b SAE level 4 ADS + SAE level 2 ADAS accidents with a sample of 2100. c SAE level 2 ADAS accidents with a sample of 1001. d SAE level 4 ADS accidents with a sample of 1099.

Fig. 3. Rear-end accident conditions between ADS and HDV.

a Rear-end accidents that HDV hit an ADS from behind with a sample of 252. b Rear-end accidents that ADS hit an HDV from behind with a sample of 67.

Fig. 4. Distribution of the pre-accidents Speed.

a ADAS Average Pre-accident Speed Heatmap. b ADS Average Pre-accident Speed Heatmap.

Fig. 5. Distribution of the accident’s types and scenarios.

a HDV accidents with a sample of 35,113. b ADS accidents with a sample of 1099. The distributions of accident types (head-on, sideswipe, rear-end, broadside) for vehicle categories (HDV and ADS) illustrate the frequency and proportion of each accident type in the respective locations by vehicle categories.

Fig. 6. Selection for the optimal number of controls.

The blue column line indicates the average coefficient changes (left y-axis), and the red line shows the changes in loglikelihood values (right y-axis).

Fig. 7. Sample size of data.

The blue fonts indicate general accident trends, while the orange fonts represent data for the matched case control model of ADS accidents in California. The HDV data is sourced from SWITRS. The ADS (SAE Level 4) data is sourced from CADMV and NHTSA, while the ADAS (SAE Level 2) data is sourced from NHTSA.