Multi-Vehicle Trajectory Design During Cooperative Adaptive Cruise Control Platoon Formation

2020 ◽  
Vol 2674 (4) ◽  
pp. 30-41 ◽  
Author(s):  
Qinzheng Wang ◽  
Xianfeng (Terry) Yang ◽  
Zhitong Huang ◽  
Yun Yuan
Keyword(s):  
Fuel Consumption ◽  
Adaptive Cruise Control ◽  
Second Phase ◽  
Trajectory Design ◽  
Cruise Control ◽  
Basic Scenario ◽  
Vehicle Trajectories ◽  
Cooperative Adaptive Cruise Control ◽  
Proposed Model ◽  
Study Results

Cooperative adaptive cruise control (CACC) organizes connected and automated vehicles (CAVs) in platoons to improve traffic flow and reduce fuel consumption. Platoon formation involves a very complex process, however, because lateral and longitudinal misbehavior of CAVs results in greater fuel consumption and risk of collision. This study aims to design optimal vehicle trajectories of CAVs during CACC platoon formation. First, a basic scenario and a destination-based protocol are described to determine vehicle sequence in the platoon. A space-time lattice based model is then formulated to construct vehicle trajectories considering boundary conditions of kinematic limits, vehicle-following safety, and lane-changing rules. The objective is to optimize the vehicle sequence and fuel consumption simultaneously. A two-phase algorithm is proposed to solve this model, where the first phase is a heuristic algorithm that determines vehicle sequence and in the second phase dynamic programming is adapted to optimize fuel consumption based on the determined sequence. To evaluate the effectiveness of the proposed model in designing CAV trajectories, extensive experimental tests have been conducted in this study. Results show that the proposed model and algorithm can effectively optimize CAV sequence in the platoon based on their destinations. After optimization, CAV fuel consumption was reduced by 42%, 46%, and 43%, respectively, in three different tested scenarios.

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

2020 ◽  
Vol 2674 (6) ◽  
pp. 253-267 ◽  
Author(s):  
Iman Mahdinia ◽  
Ramin Arvin ◽  
Asad J. Khattak ◽  
Amir Ghiasi
Keyword(s):  
Fuel Consumption ◽  
Adaptive Cruise Control ◽  
Field Tests ◽  
System Level ◽  
Level Energy ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Cooperative Adaptive Cruise Control ◽  
Vehicle Platoon ◽  
Fuel Consumption And Emissions

Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

scholarly journals Realistic Car-Following Models for Microscopic Simulation of Adaptive and Cooperative Adaptive Cruise Control Vehicles

2017 ◽  
Vol 2623 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Lin Xiao ◽  
Meng Wang ◽  
Bart van Arem
Keyword(s):  
Adaptive Cruise Control ◽  
Model Performance ◽  
Cruise Control ◽  
Vehicle Collisions ◽  
Car Following ◽  
Cooperative Adaptive Cruise Control ◽  
Proposed Model ◽  
Wide Range ◽  
Car Following Model ◽  
Vehicle Response

Adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) are important technologies for the achievement of vehicle automation, and their effect on traffic systems generally is evaluated with microscopic traffic simulations. A successful simulation requires realistic vehicle behavior and minimal vehicle collisions. However, most existing ACC-CACC simulation studies used simplified models that were not based on real vehicle response. The studies rarely addressed collision avoidance in the simulation. The study presented in this paper developed a realistic and collision-free car-following model for ACC-CACC vehicles. A multiregime model combining a realistic ACC-CACC system with driver intervention for vehicle longitudinal motions is proposed. This model assumes that a human driver resumes vehicle control either according to his or her assessment or after a collision warning asks the driver to take over. The proposed model was tested in a wide range of scenarios to explore model performance and collision possibilities. The testing scenarios included three regular scenarios of stop-and-go, approaching, and cut-out maneuvers, as well as two extreme safety-concerned maneuvers of hard brake and cut-in. The simulation results show that the proposed model is collision free in the full-speed-range operation with leader accelerations within −1 to 1 m/s2 and in approaching and cut-out scenarios. Those results indicate that the proposed ACC-CACC car-following model can produce realistic vehicle response without causing vehicle collisions in regular scenarios for vehicle string operations.

Multiobjective Optimization Framework for Cooperative Adaptive Cruise Control Vehicles in the Automated Vehicle Platooning Environment

2017 ◽  
Vol 2625 (1) ◽  
pp. 32-42 ◽  
Author(s):  
Zijia Zhong ◽  
Joyoung Lee ◽  
Liuhui Zhao
Keyword(s):  
Multiobjective Optimization ◽  
Fuel Consumption ◽  
Adaptive Cruise Control ◽  
Control Method ◽  
Cruise Control ◽  
Longitudinal Control ◽  
Target Time ◽  
Cooperative Adaptive Cruise Control ◽  
Vehicle Platoon ◽  
Unsafe Condition

Automated longitudinal control technology has been tested through cooperative adaptive cruise control (CACC), which is envisioned to improve highway mobility drastically by forming a vehicle platoon with short headway while maintaining stable traffic flow under disturbances. Compared with previous research efforts with the pseudomultiobjective optimization process, this paper proposes an automated longitudinal control framework based on multiobjective optimization (MOOP) for CACC by taking into consideration four optimization objectives: mobility, safety, driver comfort, and fuel consumption. Of the target time headways that have been tested, the proposed CACC platoon control method achieved the best performance with 0.9- and 0.6-s target time headways. Compared with a non-optimization-based CACC, the MOOP CACC achieved 98%, 93%, 42%, and 33% objective value reductions of time headway deviation, unsafe condition, jitter, and instantaneous fuel consumption, respectively. In comparison with a single-objective-optimization-based approach, which optimized only one of the four proposed objectives, it was shown that the MOOP-based CACC maintained a good balance between all of the objective functions and achieved Pareto optimality for the entire platoon.

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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