scholarly journals Hybrid Car-Following Strategy Based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control

2021 ◽  
pp. 1-9
Author(s):  
Ruidong Yan ◽  
Rui Jiang ◽  
Bin Jia ◽  
Jin Huang ◽  
Diange Yang
Keyword(s):  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Car Following ◽  
Hybrid Car ◽  
Cooperative Adaptive Cruise Control ◽  
Policy Gradient

Evaluation of Driver Car-Following Behavior Models for Cooperative Adaptive Cruise Control Systems

2017 ◽  
Vol 2622 (1) ◽  
pp. 84-95 ◽  
Author(s):  
Mizanur Rahman ◽  
Mashrur Chowdhury ◽  
Kakan Dey ◽  
M. Rafiul Islam ◽  
Taufiquar Khan
Keyword(s):  
Vehicle Dynamics ◽  
Adaptive Cruise Control ◽  
User Acceptance ◽  
Evaluation Framework ◽  
Cruise Control ◽  
String Stability ◽  
Maximum Acceleration ◽  
Car Following ◽  
Cooperative Adaptive Cruise Control ◽  
Behavior Models

A cooperative adaptive cruise control (CACC) system targeted to obtain a high level of user acceptance must replicate the driving experience in each CACC vehicle without compromising the occupant’s comfort. “User acceptance” can be defined as the safety and comfort of the occupant in the CACC vehicle in terms of acceptable vehicle dynamics (i.e., the maximum acceleration or deceleration) and string stability (i.e., the fluctuations in the vehicle’s position, speed, and acceleration). The primary objective of this study was to develop an evaluation framework for the application of a driver car-following behavior model in CACC system design to ensure user acceptance in terms of vehicle dynamics and string stability. The authors adopted two widely used driver car-following behavior models, ( a) the optimum velocity model (OVM) and ( b) the intelligent driver model (IDM), to prove the efficacy of the evaluation framework developed in this research for CACC system design. A platoon of six vehicles was simulated for three traffic flow states (uniform speed, speed with constant acceleration, and speed with constant deceleration) with different acceleration and deceleration rates. The maximum acceleration or deceleration and the sum of the squares of the errors of the follower vehicle speed were measured to evaluate user acceptance in terms of vehicle dynamics and string stability. Analysis of the simulation results revealed that the OVM performed better at modeling a CACC system than did the IDM in terms of acceptable vehicle dynamics and string stability.

Fuel consumptions and exhaust emissions induced by cooperative adaptive cruise control strategies

2015 ◽  
Vol 29 (14) ◽  
pp. 1550084 ◽  
Author(s):  
Shaowei Yu ◽  
Zhongke Shi
Keyword(s):  
Adaptive Cruise Control ◽  
Control Strategies ◽  
Exhaust Emissions ◽  
Cruise Control ◽  
Difference Model ◽  
Velocity Difference ◽  
Car Following ◽  
Road Traffic Safety ◽  
Cooperative Adaptive Cruise Control ◽  
Car Following Model

Many cooperative adaptive cruise control strategies have been presented to improve traffic efficiency as well as road traffic safety, but scholars have rarely explored the impacts of these strategies on cars' fuel consumptions and exhaust emissions. In this paper, we respectively select two-velocity difference model, multiple velocity difference model and the car-following model considering multiple preceding cars' accelerations to investigate each car's fuel consumptions, carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides ( NO X ) emissions and carry out comparative analysis. The comparisons of fuel consumptions and exhaust emissions in three different cruise control strategies show that cooperative cars simulated by the car-following model considering multiple preceding cars' accelerations can run with the minimal fuel consumptions, CO, HC and NO X emissions, thus, taking the car-following model considering multiple preceding cars' accelerations as the cooperative adaptive cruise control strategy can significantly improve cars' fuel efficiency and exhaust emissions.

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.

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.

scholarly journals A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

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