scholarly journals Evaluating Connected Vehicles and Their Applications

2016 ◽  
Vol 138 (12) ◽  
pp. S12-S17 ◽  
Author(s):  
Mohd Azrin Mohd Zulkefli ◽  
Pratik Mukherjee ◽  
Yunli Shao ◽  
Zongxuan Sun
Keyword(s):  
Real Time ◽  
Energy Savings ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
New Paradigm ◽  
Target Vehicle ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Research Platform

This article presents evaluation results of connected vehicles and their applications. Vehicle-to-vehicle communication (V2V) and vehicle-to-infrastructure communication (V2I) can enable a new paradigm of vehicle applications. The connected vehicle applications could significantly improve vehicle safety, mobility, energy savings, and productivity by utilizing real-time vehicle and traffic information. In the foreseeable future, connected vehicles need to operate alongside unconnected vehicles. This makes the evaluation of connected vehicles and their applications challenging. The hardware-in-the-loop (HIL) testbed can be used as a tool to evaluate the connected vehicle applications in a safe, efficient, and economic fashion. The HIL testbed integrates a traffic simulation network with a powertrain research platform in real time. Any target vehicle in the traffic network can be selected so that the powertrain research platform will be operated as if it is propelling the target vehicle. The HIL testbed can also be connected to a living laboratory where actual on-road vehicles can interact with the powertrain research platform.

scholarly journals Vehicle and Powertrain Optimization for Autonomous and Connected Vehicles

2017 ◽  
Vol 139 (09) ◽  
pp. S19-S23 ◽  
Author(s):  
Yunli Shao ◽  
Mohd Azrin Mohd Zulkefli ◽  
Zongxuan Sun
Keyword(s):  
Energy Savings ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Traffic Information ◽  
Connected Vehicle ◽  
V2v Communication ◽  
Vehicle To Vehicle ◽  
Potential Benefits ◽  
Vehicle To Infrastructure ◽  
Save Energy

This article discusses the potential of using autonomous and connected vehicle (CV) technologies to save energy. It also focuses on the potential energy savings of internal combustion engine-based vehicles (ICVs) and hybrid electric vehicles (HEVs). An example of vehicle and powertrain co-optimization for HEV eco-approaching and departure is also given. CV technologies are gaining increasing attention around the world. Vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication enable real-time access to traffic information that was not available before, including preceding vehicles’ location, speed, pedal position, traffic signal phasing and timing (SPaT). The example shown in this article demonstrates the potential benefits from vehicle and powertrain co-optimization by investigating an eco-approaching and departure application. More research in this area can offer more mature solutions to implement such optimization in a real-production vehicle.

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

Low Cost and Modular Approach to Real-Time Vehicle to Vehicle Communication using Wi-Fi

2021 ◽  
Author(s):  
Sanjay M Santhosh ◽  
S Hari Sankar ◽  
G Balagopal ◽  
A U Jayakrishnan ◽  
Shehan P Rajendran ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Modular Approach ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Vehicle To Vehicle Communication

scholarly journals Microscopic Estimation of Freeway Vehicle Positions from the Behavior of Connected Vehicles

2020 ◽  
Author(s):  
Noah J. Goodall ◽  
Brian L. Smith ◽  
Byungkyu Brian Park
Keyword(s):  
Traffic Signals ◽  
The United States ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Travel Time Prediction ◽  
High Penetration ◽  
Initial Penetration ◽  
Car Following Model ◽  
Status Data

Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicle's acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates.

Real-Time Powertrain Optimization Strategy for Connected Hybrid Electrical Vehicle

2016 ◽  
Author(s):  
Mohd Azrin Mohd Zulkefli ◽  
Zongxuan Sun
Keyword(s):  
Real Time ◽  
Numerical Solutions ◽  
Computation Time ◽  
Computational Effort ◽  
Linear Functions ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Optimization Strategy ◽  
Separable Programming

Connected Vehicle (CV) technology, which allows traffic information sharing, and Hybrid Electrical Vehicles (HEV) can be combined to improve vehicle fuel efficiency. However, transient traffic information in CV environment necessitates a fast HEV powertrain optimization for real-time implementation. Model Predictive Control (MPC) with Linearization is proposed, but the computational effort is still prohibitive. The Equivalent Consumption Minimization Strategy (ECMS) and Adaptive-ECMS are proposed to minimize computation time, but unable to guarantee charge-sustaining-operation (CS). Fast analytical result from Pontryagin’s Minimum Principles (PMP) is possible but the input has to be unconstrained. Numerical solutions with Linear Programming (LP) are proposed, but over-simplifications of the cost and constraint functions limit the performance of such methods. In this paper, a nonlinear CS constraint is transformed into linear form with input variable change. With linear input and CS constraints, the problem is solved with Separable Programming by approximating the nonlinear cost with accurate linear piecewise functions which are convex. The piecewise-linear functions introduce new dimensionless variables which are solved as a large-dimension constrained linear problem with efficient LP solvers. Comparable fuel economy with Dynamic Programming (DP) is shown, with maximum fuel savings of 7% and 21.4% over PMP and Rule-Based (RB) optimizations. Simulations with different levels of vehicle speed prediction uncertainties to emulate CV settings are presented.

Designing Network Motifs in Connected Vehicle Systems: Delay Effects and Stability

2013 ◽  
Author(s):  
Linjun Zhang ◽  
Gábor Orosz
Keyword(s):  
Nearest Neighbor ◽  
Connected Vehicle ◽  
Cruise Control ◽  
Long Distance ◽  
Analysis And Design ◽  
Vehicle Communication ◽  
V2v Communication ◽  
Vehicle To Vehicle ◽  
Distance Communication ◽  
Vehicle Platoons

Arising technologies in vehicle-to-vehicle (V2V) communication allow vehicles to obtain information about the motion of distant vehicles. Such information can be presented to the driver or incorporated in advanced autonomous cruise control (ACC) systems. In this paper, we investigate the effects of multi-vehicle communication on the dynamics of connected vehicle platoons and propose a motif-based approach that allows systematical analysis and design of such systems. We investigate the dynamics of simple motifs in the presence of communication delays, and show that long-distance communication can stabilize the uniform flow when the flow cannot be stabilized by nearest neighbor interactions. The results can be used for designing driver assist systems and communication-based cruise control systems.

scholarly journals DEVELOPMENT OF WEBGIS BASED REAL TIME ROAD TRAFFIC INFORMATION SYSTEM

2018 ◽  
Vol IV-5 ◽  
pp. 37-45
Author(s):  
H. Shankar ◽  
M. Sharma ◽  
K. Oberai ◽  
S. Saran
Keyword(s):  
Information System ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Cost ◽  
Traffic Information ◽  
Traffic Density ◽  
Vehicle To Vehicle ◽  
Decision Making Processes ◽  
Inductive Loop Detector

<p><strong>Abstract.</strong> Rapid increase in road traffic density results into a serious problem of Traffic Congestion (TC) in cities. During peaks hours TC is very high and hence public search least congested path for their journeys in order to minimize ravel time and hence transportation cost. In this study, a new empirical model was developed to estimate congestion levels using real time road Traffic Parameters (TPs) such as vehicle density, speed, class and vehicle-to-vehicle (V2V) gap. These real time road TPs were collected using latest generation Inductive Loop Detector (ILD) technology. Further, a WebGIS based Road Traffic Information System (RTIS) for Dehradun city was developed for real time TD analyses and visualisation. This RTIS is very useful for public and user departments for planning and decision making processes. No other such system is available in India, which handles multiple traffic parameters simultaneously to provide solution of day-to-day problems.</p>

scholarly journals Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2059 ◽  
Author(s):  
Kai Gao ◽  
Farong Han ◽  
Pingping Dong ◽  
Naixue Xiong ◽  
Ronghua Du
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
Queue Length ◽  
Penetration Rate ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Mixed Traffic ◽  
Model Based ◽  
Sensing Model

With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

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