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Article

Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments

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
*
Author to whom correspondence should be addressed.
Smart Cities 2023, 6(5), 2260-2281; https://doi.org/10.3390/smartcities6050104
Submission received: 12 July 2023 / Revised: 24 August 2023 / Accepted: 28 August 2023 / Published: 29 August 2023
(This article belongs to the Section Smart Urban Mobility, Transport, and Logistics)

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.
Keywords: autonomous vehicles; car following model; safety; smart cities; mixed-driver environment autonomous vehicles; car following model; safety; smart cities; mixed-driver environment

Share and Cite

MDPI and ACS Style

Yang, X.; Ahemd, H.U.; Huang, Y.; Lu, P. Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments. Smart Cities 2023, 6, 2260-2281. https://doi.org/10.3390/smartcities6050104

AMA Style

Yang X, Ahemd HU, Huang Y, Lu P. Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments. Smart Cities. 2023; 6(5):2260-2281. https://doi.org/10.3390/smartcities6050104

Chicago/Turabian Style

Yang, Xinyi, Hafiz Usman Ahemd, Ying Huang, and Pan Lu. 2023. "Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments" Smart Cities 6, no. 5: 2260-2281. https://doi.org/10.3390/smartcities6050104

APA Style

Yang, X., Ahemd, H. U., Huang, Y., & Lu, P. (2023). Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments. Smart Cities, 6(5), 2260-2281. https://doi.org/10.3390/smartcities6050104

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