Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections

2021 ◽  
Vol 92 ◽  
pp. 102746
Author(s):  
Fangwu Ma ◽  
Yu Yang ◽  
Jiawei Wang ◽  
Xinchen Li ◽  
Guanpu Wu ◽  
...  
Keyword(s):  
Adaptive Cruise Control ◽  
Signalized Intersections ◽  
Connected Vehicles ◽  
Cruise Control ◽  
Cooperative Adaptive Cruise Control

scholarly journals Battery Electric Vehicle Eco-Cooperative Adaptive Cruise Control in the Vicinity of Signalized Intersections

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2433
Author(s):  
Hao Chen ◽  
Hesham A. Rakha
Keyword(s):  
Energy Consumption ◽  
Adaptive Cruise Control ◽  
Combustion Engine ◽  
Signalized Intersections ◽  
Simulation Software ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Optimal Speed ◽  
Cooperative Adaptive Cruise Control ◽  
The Impact

This study develops a connected eco-driving controller for battery electric vehicles (BEVs), the BEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory is formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location, and signal timing, defined by vehicle characteristics and signal phase and timing (SPaT) data shared under a connected vehicle environment. The optimal speed trajectory is computed in real-time by the proposed BEV eco-CACC-I controller, so that a BEV can follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate its performance under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays.

Robust Cooperative Adaptive Cruise Control Design for Connected Vehicles

2015 ◽  
Author(s):  
Mark Trudgen ◽  
Javad Mohammadpour
Keyword(s):  
Traffic Congestion ◽  
Adaptive Cruise Control ◽  
Connected Vehicles ◽  
Cruise Control ◽  
String Stability ◽  
Reduction Techniques ◽  
Stability Robustness ◽  
Cooperative Adaptive Cruise Control ◽  
And Performance ◽  
Viable Approach

In this paper, we design and validate a robust H∞ controller for Cooperative Adaptive Cruise Control (CACC) in connected vehicles. CACC systems take advantage of onboard sensors and wireless technologies working together in order to achieve smaller inter-vehicle following distances, with the overall goal of increasing vehicle throughput on busy highways, and hence serving as a viable approach to reduce traffic congestion. A group of connected vehicles equipped with CACC technology must also ensure what is known as string stability. This requirement effectively dictates that disturbances should be attenuated as they propagate along the platoon of following vehicles. In order to guarantee string stability and to cope with the uncertainties seen in the vehicle model used for a model-based CACC, we propose to design and implement a robust H∞ controller. Loop shaping design methodology is used in this paper to achieve desired tracking characteristics in the presence of competing string stability, robustness and performance requirements. We then employ model reduction techniques to reduce the order of the controller and finally implement the reduced-order controller on a simulation model demonstrating the robust properties of the closed-loop system.

Connected Vehicle-Cooperative Adaptive Cruise Control Algorithm to Divide and Reform Connected Vehicle Platoons at Signalized Intersections to Improve Traffic Throughput and Safety

2021 ◽  
pp. 036119812110054
Author(s):  
Yuwei Bie ◽  
Tony Z. Qiu
Keyword(s):  
Adaptive Cruise Control ◽  
Control Variable ◽  
Signalized Intersections ◽  
Connected Vehicle ◽  
Cruise Control ◽  
Cooperative Adaptive Cruise Control ◽  
Before And After ◽  
Vehicle Platoons ◽  
Real Time Information ◽  
Time Information

The cooperative adaptive cruise control (CACC) algorithm is a simple and effective way to form small-headway platoons so that road capacity and traffic throughput can be improved. The CACC algorithm has been broadly discussed in relation to the highway driving environment where frequent stopping and merging are uncommon. This paper proposes that CACC can also benefit urban arterials, using the appropriate algorithm to predict platoon behavior with optimized trajectories to divide and reform platoons before and after signalized intersections, thus maintaining small, safe headways. Connected vehicle (CV) technology is the key to adapting and improving the CACC algorithm, as it enables the signal phasing plan to be sent to a target CACC platoon and allows vehicles to acquire real-time information from other vehicles in the platoon. In this research, a CV-CACC algorithm is proposed consisting of two functions: platoon division and platoon reforming. The new algorithm is also equipped with acceleration as a new control variable instead of speed, so that the platoon is able to accommodate sharp speed changes around intersections, something the baseline CACC is unable to accommodate. In this study, computer simulations have been conducted to test the reliability of the CV-CACC algorithm and compare its performance against the baseline CACC algorithm.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

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