Connect & Drive:design and evaluation of cooperative adaptive cruise control for congestion reduction

Road throughput can be increased by driving at small inter-vehicle time gaps. The amplification of velocity disturbances in upstream direction, however, poses limitations to the minimum feasible time gap. This effect is covered by the notion of string stability. String-stable behavior is thus considered an essential requirement for the design of automatic distance control systems, which are needed to allow for safe driving at time gaps well below 1 s. Using wireless inter-vehicle communications to provide real-time information of the preceding vehicle, in addition to the information obtained by common Adaptive Cruise Control （ACC） sensors, appears to significantly decrease the feasible time gap, which is shown by practical experiments with a test fleet consisting of six passenger vehicles. The large-scale deployment of this system, known as Cooperative ACC （CACC）, however, poses challenges with respect to the reliability of the wireless communication system. A solution for this scalability problem can be found in decreasing the transmission power and/or beaconing rate, or adapting the communications protocol. Although the main CACC objective is to increase road throughput, the first commercial application of CACC is foreseen to be in truck platooning, since short distance following is expected to yield significant fuel savings in this case.

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion.In recent years,several cooperative driving approaches for idealized traffic scenarios(i.e.,uniform vehicle arrivals,lengths,and speeds)have been proposed.However,theoretical analyses and comparisons of these approaches are lacking.In this study,we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance.We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles.The obtained conclusions shed light on future cooperative driving studies.

Study on driver’s braking intention identification based on functional near-infrared spectroscopy

Purpose–Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together.One of the key technologies is that the intelligent system can identify the driver’s driving intention in real time to implement consistent driving decisions.The purpose of this study is to establish a driver intention prediction model.Design/methodology/approach–The authors used the NIRx device to measure the cerebral cortex activities for identifying the driver’s braking intention.The experiment was carried out in a virtual reality environment.During the experiment,the driving simulator recorded the driving data and the functional near-infrared spectroscopy(fNIRS)device recorded the changes in hemoglobin concentration in the cerebral cortex.After the experiment,the driver’s braking intention identification model was established through the principal component analysis and back propagation neural network.Findings–The research results showed that the accuracy of the model established in this paper was 80.39 per cent.And,the model could identify the driver’s braking intent prior to his braking operation.Research limitations/implications–The limitation of this study was that the experimental environment was ideal and did not consider the surrounding traffic.At the same time,other actions of the driver were not taken into account when establishing the braking intention recognition model.Besides,the verification results obtained in this paper could only reflect the results of a few drivers’identification of braking intention.Practical implications–This study can be used as a reference for future research on driving intention through fNIRS,and it also has a positive effect on the research of brain-controlled driving.At the same time,it has developed new frontiers for intention recognition of cooperative driving.Social implications–This study explores new directions for future brain-controlled driving and wheelchairs.Originality/value–The driver’s driving intention was predicted through the fNIRS device for the first time.

"Charge while driving"for electric vehicles:road traffic modeling and energy assessment

The aim of this research study is to present a method for analyzing the performance of the wireless inductive charge-while-driving(CWD)electric vehicles,from both traffic and energy points of view.To accurately quantify the electric power required from an energy supplier for the proper management of the charging system,a traffic simulation model is implemented.This model is based on a mesoscopic approach,and it is applied to a freight distribution scenario.Lane changing and positioning are managed according to a cooperative system among vehicles and supported by advanced driver assistance systems(ADAS).From the energy point of view,the analyses indicate that the traffic may have the following effects on the energy of the system:in a low traffic level scenario,the maximum power that should be supplied for the entire road is simulated at approximately 9 MW;and in a high level traffic scenario with lower average speeds,the maximum power required by the vehicles in the charging lane increases by more than 50%.