scholarly journals Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy

2018 ◽  
Vol 8 (6) ◽  
pp. 978 ◽  
Author(s):  
Junhui Zhang ◽  
Qing Li ◽  
Dapeng Chen
Keyword(s):  
Adaptive Cruise Control ◽  
Weight Coefficient ◽  
Cruise Control ◽  
Self Tuning ◽  
Tuning Strategy

scholarly journals Research on Vehicle Adaptive Cruise Control Method Based on Fuzzy Model Predictive Control

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 160
Author(s):  
Jin Mao ◽  
Lei Yang ◽  
Yuanbo Hu ◽  
Kai Liu ◽  
Jinfu Du
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Control Method ◽  
Lower Layer ◽  
Fuzzy Model ◽  
Optimization Method ◽  
Weight Coefficient ◽  
Cruise Control ◽  
Fixed Weight

Under complex working conditions, vehicle adaptive cruise control (ACC) systems with fixed weight coefficients cannot guarantee good car following performance under all conditions. In order to improve the tracking and comfort of vehicles in different modes, a fuzzy model predictive control (Fuzzy-MPC) algorithm is proposed. Based on the comprehensive consideration of safety, comfort, fuel economy and vehicle limitations, the objective function and constraints are designed. A relaxation factor vector is introduced to soften the hard constraint boundary in order to solve this problem, for which there was previously no feasible solution. In order to maintain driving stability under complex conditions, a multi-objective optimization method, which can update the weight coefficient online, is proposed. In the numerical simulation, the values of velocity, relative distance, acceleration and acceleration change rate under different conditions are compared, and the results show that the proposed algorithm has better tracking and stability than the traditional algorithm. The effectiveness and reliability of the Fuzzy-MPC algorithm are verified by co-simulation with the designed PID lower layer control algorithm with front feedforward and feedback.

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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