Affiliation:
1. Department of Civil and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA
2. Department of Transportation, Logistics and Finance, North Dakota State University, Fargo, ND 58102, USA
Abstract
The contribution of autonomous vehicles to traffic is one of the key aspects of future ground transportation in smart cities. Autonomous vehicles are able to provide many benefits, but some benefits can only provide advantages if these vehicles comprise a large percent of on the road/driven vehicles, which may take decades. Until then, the robotic drivers in autonomous vehicles will share the same road system with human divers in a mixed-driver environment where the majority of road accidents for autonomous vehicles are associated with the operational inconsistency of human drivers. In this paper, a cumulatively anticipative car-following model (which considers cumulative influences from multiple preceding vehicles) is developed to potentially improve the safety of autonomous vehicles in mixed-driver environments that benefit from enhanced communication between the autonomous vehicles and other components in the transportation system. Through intensive simulations (200 simulations), this study comprehensively evaluates four models including the cumulative anticipative car-following model, the Wiedemann 99 model, adaptive cruise control, and the cooperative adaptive cruise control model. Across 10 scenarios and five speed limits (24.59–33.53 m/s), the cumulative anticipative car-following model consistently demonstrates superior conflict reduction, with average, maximum, and minimum conflict percentages ranging from 77.69% to 91.97% against the Wiedemann 99 model, 67.00% to 93.94% against the adaptive cruise control model, and 69.17% to 93.25% against the cooperative adaptive cruise control model. Notably, the cooperative adaptive cruise control model exhibits suboptimal performance, especially in mixed-driver settings. The cumulative anticipative car-following model also enhances vehicle mobility, reducing average stops by up to 93.54%, 91.74%, 92.04%, 88.60%, and 91.35% in comparison to the other three models at speeds of 24.59 m/s, 26.82 m/s, 29.06 m/s, 31.29 m/s, and 33.53 m/s. Overall, the cumulative anticipative car-following model holds significant potential for conflict reduction and traffic enhancement.
Funder
U.S. Department of Transportation
MPC projects
Subject
Electrical and Electronic Engineering,Artificial Intelligence,Urban Studies
Reference47 articles.
1. (2018). Sae International Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. SAE Int., 4970, 1–5.
2. Tang, Q., Dagley, G., Ghamsari, A., Price, M., and Hoover, J. (2020). Automatic Vehicle Configuration Based on Sensor Data. (10,525,850), U.S. Patent.
3. Automatic Vehicle Classification System with Range Sensors;Harlow;Transp. Res. Part C Emerg. Technol.,2001
4. An Automatic Lane Identification Method for the Roadside Light Detection and Ranging Sensor;Wu;J. Intell. Transp. Syst.,2020
5. Automatic Multiple Moving Humans Detection and Tracking in Image Sequences Taken from a Stationary Thermal Infrared Camera;Younsi;Expert Syst. Appl.,2020
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