Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

Abstract

Purpose This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline. Design/methodology/approach Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms. Findings Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles. Originality/value To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

Spatiotemporal Interaction Pattern Recognition and Risk Evolution Analysis During Lane Changes

“…O can be called a LC interaction scenario, where 𝑜 𝑡 = {𝑥 𝑡 (1) , 𝑦 𝑡 (1) , 𝑥 𝑡 (2) , 𝑦 𝑡 (2) , 𝑥 𝑡 (3) , 𝑦 𝑡 (3) } ∈ ℝ 6 . 𝑥 𝑡 (1) , 𝑥 𝑡 (2) and 𝑥 𝑡 (3) are the longitudinal position of three vehicles at time t, respectively.…”

“…O can be called a LC interaction scenario, where 𝑜 𝑡 = {𝑥 𝑡 (1) , 𝑦 𝑡 (1) , 𝑥 𝑡 (2) , 𝑦 𝑡 (2) , 𝑥 𝑡 (3) , 𝑦 𝑡 (3) } ∈ ℝ 6 . 𝑥 𝑡 (1) , 𝑥 𝑡 (2) and 𝑥 𝑡 (3) are the longitudinal position of three vehicles at time t, respectively. 𝑦 𝑡 (1) , 𝑦 𝑡 (2) and 𝑦 𝑡 (3) are the lateral position of three vehicles at time t. T represents the time length of a LC scenario O (or the duration of a LC event), and t represents any timestamp during LC.…”

“…𝑥 𝑡 (1) , 𝑥 𝑡 (2) and 𝑥 𝑡 (3) are the longitudinal position of three vehicles at time t, respectively. 𝑦 𝑡 (1) , 𝑦 𝑡 (2) and 𝑦 𝑡 (3) are the lateral position of three vehicles at time t. T represents the time length of a LC scenario O (or the duration of a LC event), and t represents any timestamp during LC. The primitive of O is formulated as Eq.…”

“…With the advantages of perception and information acquisition, autonomous vehicles can significantly reduce traffic crashes. However, how autonomous vehicles make reasonable decisions in complex LC scenarios is a major challenge at present [3,4]. On the one hand, human behavior is heterogeneous and stochastic.…”

Optimal Deployment of Dynamic Wireless Charging Lanes for Electric Vehicles considering the Battery Charging Rate

“…Most of the previous studies are roughly linear between the maximum and minimum charging points [36,42,43,53]. Considering the complicated nonlinearity spectrum of battery charging, some simulation results have been obtained for the health-aware fast charging strategy for lithium-ion batteries (LIBs) [54,55]. In the nonlinear charging function, the charging speed decreases approximately linearly over time [36,[56][57][58].…”