Safety, Mobility, and Environmental Impacts of Driver-Assistance-Enabled Electric Vehicles: An Empirical Study

TL;DR

This study addresses how ACC-enabled EVs compare to ICEVs in safety, mobility, and environmental impact using real-world OpenACC data and DTW-based trajectory alignment. It develops a three-part empirical framework assessing efficiency ($ASV$ and velocity–spacing curves), safety (TTC and DRAC with a critical-event rate), and environmental effects via the VT-Micro model. Key findings show ACC-enabled EVs deliver smoother following, tighter yet stable headways (lower critical spacing), substantially fewer safety-critical events at low TTC/DRAC thresholds (e.g., ~85–86% reductions), and lower ICEV-followed emissions in EV-led platoons (up to 26.2% for NO$_x$ and notable reductions for HC, CO, and fuel consumption). These results imply electrification can partially offset ACC limitations, guiding policy and design for mixed fleets and informing future work on lateral dynamics and broader real-world validity.

Abstract

The advancement of vehicle automation and the growing adoption of electric vehicles (EVs) are reshaping transportation systems. While fully automated vehicles are expected to improve traffic stability, efficiency, and sustainability, recent studies suggest that partially automated vehicles, such as those equipped with adaptive cruise control (ACC), may adversely affect traffic flow. These drawbacks may not extend to ACC-enabled EVs due to their distinct mechanical characteristics, including regenerative braking and smoother torque delivery. As a result, the impacts of EVs operating under ACC remain insufficiently understood. To address this gap, this study develops an empirical framework using the OpenACC dataset to compare ACC-enabled EVs and internal combustion engine vehicles. Dynamic time warping aligns comparable lead-vehicle trajectories. Results show that EVs exhibit smoother speed profiles, lower speed variability, and shorter spacing, leading to higher efficiency. EVs reduce critical safety events by over 85% and lower platoon-level emissions by up to 26.2%.

Figures (11)

• Figure 1: Data processing in the OpenACC dataset. A platoon (top) is decomposed into multiple leader–follower pairs (bottom). Specifically, the bottom row illustrates all four possible pairings, ordered from left to right: EV follower behind EV leader, ICEV follower behind EV leader, EV follower behind ICEV leader, and ICEV follower behind ICEV leader. Each pair serves as a unit of analysis for safety and efficiency evaluations.
• Figure 2: Analytical framework of the study. The empirical analysis branches into three key impact areas: efficiency, safety, and the environment. Each branch connects to the corresponding quantitative metrics used to evaluate the influence of driver-assist enabled EVs and ICEVs.
• Figure 3: Representative schematic velocity–spacing ($v$--$s$) curve. The piecewise linear fit is shown in black. Critical points are highlighted in red: the jam spacing indicates the minimum spacing at which vehicle speed approaches zero, the free-flow speed marks the asymptotic speed as spacing increases, and the critical spacing denotes the transition between car-following and free-flow regimes. This schematic illustrates the general features of $v$--$s$ relationships and car-following behavior.
• Figure 4: Leader–follower pairs used for analysis. Each pair includes a lead vehicle and a following vehicle (EV or ICEV) for safety and efficiency evaluations.
• Figure 5: Normalized DTW matrix used to assess trajectory similarity among 20 lead vehicles (10 EVs and 10 ICEVs) for efficiency comparison. Each cell represents the normalized DTW distance between a pair of speed trajectories, with green indicating high similarity (lower distance) and red indicating greater dissimilarity. The matrix is scaled between 0 (identical trajectories) and the maximum DTW value observed among the 277 vehicle pairs evaluated in this study. Due to space constraints, only the subset with the lowest median DTW distances is shown.