Performance Evaluation of Cruise Controls and Their Impact on Passenger Comfort in Autonomous Vehicle Platoons

Author(s):  
Rahi Avinash Shet ◽  
Frederik Schewe
Author(s):  
T. Farid ◽  
A. Shakeel ◽  
M. Sajid

Abstract The ever-growing road congestion and safety hazards induced by conventional highways has inspired the development of automated highways which provides four key benefits: fuel economy, environmental protection, road safety and smooth traffic flow. Vehicle platooning is a vital component of automated highways which contributes directly to these four benefits with its sequence of closely spaced leader-follower vehicle configuration by taking advantage of the ‘slip-stream’ effect to minimize the aerodynamic drag. Exploratory studies into platooning parameters, vehicle spacing, speeds and number of vehicles, have proven to be prohibitive expensive both computationally and experimentally due to the complexity of tests and the large number of test cases. In recent years, OpenFOAM® an independently developed, supported and documented open-source toolbox has gained popularity by offering a lower cost alternative to leading commercial CFD products. This paper summarizes the results from a computational study of autonomous vehicle platoons and the capability of OpenFOAM® to substitute leading commercial CFD solutions currently used to support vehicle aerodynamic development. This study investigates the aerodynamic characteristics of a 4-SUV platoon at inter-vehicle distances ranging from 0.25 to 1 SUV length at a constant speed of 23 m/s. Trends of the predicted aerodynamic drag coefficients (Cd) are then compared against experimental data from published literature as well as the results obtained from a leading commercial CFD package.


Author(s):  
Chang Wang ◽  
Xia Zhao ◽  
Rui Fu ◽  
Zhen Li

Comfort is a significant factor that affects passengers’ choice of autonomous vehicles. The comfort of an autonomous vehicle is largely determined by its control algorithm. Therefore, if the comfort of passengers can be predicted based on factors that affect comfort and the control algorithm can be adjusted, it can be beneficial to improve the comfort of autonomous vehicles. In view of this, in the present study, a human-driven experiment was carried out to simulate the typical driving state of a future autonomous vehicle. In the experiment, vehicle motion parameters and the comfort evaluation results of passengers with different physiological characteristics were collected. A single-factor analysis method and binary logistic regression analysis model were used to determine the factors that affect the evaluation results of passenger comfort. A passenger comfort prediction model was established based on the bidirectional long short-term memory network model. The results demonstrate that the accuracy of the passenger comfort prediction model reached 84%, which can provide a theoretical basis for the adjustment of the control algorithm and path trajectory of autonomous vehicles.


2016 ◽  
Vol 24 (5) ◽  
pp. 495-503 ◽  
Author(s):  
Heungseok Chae ◽  
Yonghwan Jeong ◽  
Kyongsu Yi ◽  
Inseong Choi ◽  
Kyongchan Min

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