Evaluation of the Energy Efficiency in a Mixed Traffic with Automated Vehicles and Human Controlled Vehicles

Gong, Xun; Guo, Yaohui; Feng, Yiheng; Sun, Jing; Zhao, Ding

doi:10.1109/itsc.2018.8569705

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…The VSP distribution of the platoon could be computed according to the VSP distribution for each vehicle. The formula was represented as Equation (17):…”

Section: Modelling the Impact Of Platoon Arrangements On Vsp Distribu...mentioning

confidence: 99%

“…For new parameters, the traffic energy consumption can be affected by other parameters such as the CAV level, penetration rate, platoon intensity, user compliance rate and communication delay [13][14][15]. Other influencing factors encompass various aspects of CAV, such as, lane changes, traffic demand and vehicle kilometres travelled (VKT) [16][17][18]. The platoon intensity was originally defined by using a Markov chain method, and further, the effect of platoon stability of CAV on traffic flow parameters was studied in the optimization process [14,19].…”

Section: Literature Reviewmentioning

confidence: 99%

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A fuel consumption model for mixed traffic flow in multiple connected and autonomous scenarios

Huang

Song

et al. 2023

IET Intelligent Trans Sys

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The potential increase/decrease in energy consumption of traffic operation modes is a hot topic in the studies of connected and autonomous vehicles (CAVs), but there are few theoretical studies on the fuel consumption of human driving vehicles (HDVs) and CAV mixed platoon as a result of limited data. Based on the power distribution of light‐duty vehicles, this study developed a fuel consumption model for HDV and CAV mixed platoons by theoretical hypothesis considering the oscillation along with the platoon. Firstly, a vehicle‐specific power (VSP) distribution conforming to normal distribution is constructed with hypothesis‐based VSP standard deviation and constant VSP average value at each speed bin under different platoon arrangement conditions. Then, the fuel factors (mL/km) are calculated by weighted fuel rate (mL/s) according to the VSP distribution. Finally, a library of fuel consumption values for multiple penetration rates, platoon intensities, and speeds was established. In the case, at a speed of 30 km/h, the fuel consumption in the CAV group was reduced by 0.8–5%, 1.9–12.5%, 8.6–12%, and 12.4%, respectively, at the penetration rate of 20%, 50%, 80%, and 100%, compared to the HDV group. The fuel consumption reduction effect of CAV was negatively correlated with the speed.

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“…The VSP distribution of the platoon could be computed according to the VSP distribution for each vehicle. The formula was represented as Equation (17):…”

Section: Modelling the Impact Of Platoon Arrangements On Vsp Distribu...mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A fuel consumption model for mixed traffic flow in multiple connected and autonomous scenarios

Huang

Song

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…The SPMD IDM parameters are used, which are calibrated from the NDD. The MOBIL model parameters are partially from Gong et al (2018) which are also calibrated using the SPMD dataset. The detailed model parameters are listed in the Appendix.…”

Section: Error Accumulation Problem For Empirical Behavior Modelsmentioning

confidence: 99%

Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing

Yan¹,

Feng²,

Sun³

et al. 2021

Preprint

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Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs). To evaluate AVs' safety performance unbiasedly, ideally, the probability distributions of the joint state space of all vehicles in the simulated naturalistic driving environment (NDE) needs to be consistent with those from the real-world driving environment. However, although human driving behaviors have been extensively investigated in the transportation engineering field, most existing models were developed for traffic flow analysis without consideration of distributional consistency of driving behaviors, which may cause significant evaluation biasedness for AV testing. To fill this research gap, a distributionally consistent NDE modeling framework is proposed. Using large-scale naturalistic driving data, empirical distributions are obtained to construct the stochastic human driving behavior models under different conditions, which serve as the basic behavior models. To reduce the model errors caused by the limited data quantity and mitigate the error accumulation problem during the simulation, an optimization framework is designed to further enhance the basic models. Specifically, the vehicle state evolution is modeled as a Markov chain and its stationary distribution is twisted to match the distribution from the real-world driving environment. In the case study of highway driving environment using realworld naturalistic driving data, the distributional accuracy of the generated NDE is validated. The generated NDE is further utilized to test the safety performance of an AV model to validate its effectiveness.

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“…The emergence of CAVs has improved driving safety and fuel economy [2]. Xun [3] suggested that the fuel economy of CAVs could be significantly improved if the surrounding vehicles are well managed. Bang [4] proposed a simple CAV control method based on the spring-mass-damper (SMD) system, which directly considers the behaviour of human-driven vehicles (HDVs) to alleviate the phenomenon of road throughput reduction caused by speed fluctuation.…”

Section: Introductionmentioning

confidence: 99%

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

Cai

Chen

et al. 2023

IET Intelligent Trans Sys

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Cooperative adaptive cruise control (CACC) realizes efficient, intelligent control of vehicle acceleration, deceleration, and steering, through inter‐vehicle communication and cooperative control. However, the close combination of the platoon makes it difficult for other vehicles to cut‐in, which can lead to severe traffic jams on certain sections of the road. The control effect of the CACC depends on the platoon penetration rate, which is the percentage of connected and autonomous vehicles (CAVs) in the total number of platoon members. There is no quantitative control method for different penetration rates, and it is difficult to quantify the impact of CACC vehicles on traffic. Therefore, this paper proposes an innovative CACC control method based on deep reinforcement learning (DRL). First, the altruism control and the quantitative control of the car‐following strategy are realized by the virtual car‐following distance method to reduce the exclusivity of the CACC platoon or improve the road utilization efficiency. Second, a more appropriate platoon reward function and collision avoidance method are proposed. Finally, the Car Learning to Act (CARLA) simulator is used. The obtained results confirm that the CACC control of CAVs based on DRL can absorb speed oscillation and improve fuel economy.

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Evaluation of the Energy Efficiency in a Mixed Traffic with Automated Vehicles and Human Controlled Vehicles

Cited by 6 publications

References 16 publications

A fuel consumption model for mixed traffic flow in multiple connected and autonomous scenarios

A fuel consumption model for mixed traffic flow in multiple connected and autonomous scenarios

Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

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