A Small, Light Radar Sensor and Control Unit for Adaptive Cruise Control

1998 ◽  
Author(s):  
Herbert Olbrich ◽  
Thomas Beez ◽  
Bernhard Lucas ◽  
Hermann Mayer ◽  
Klaus Winter
Keyword(s):  
Adaptive Cruise Control ◽  
Control Unit ◽  
Cruise Control ◽  
Radar Sensor ◽  
And Control

scholarly journals Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

2021 ◽  
Vol 13 (8) ◽  
pp. 4572
Author(s):  
Jiří David ◽  
Pavel Brom ◽  
František Starý ◽  
Josef Bradáč ◽  
Vojtěch Dynybyl
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Cruise Control ◽  
Control Unit ◽  
Driving Safety ◽  
Model Parameters ◽  
Cruise Control ◽  
Artificial Neural ◽  
Dynamic Obstacle ◽  
Braking Process

This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

Safe Adaptive Cruise Control with Control Barrier Function and Smith Predictor

2020 ◽  
Author(s):  
Caio I. G. Chinelato ◽  
Bruno A. Angélico
Keyword(s):  
Numerical Simulations ◽  
Barrier Function ◽  
Adaptive Cruise Control ◽  
Smith Predictor ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Control Lyapunov Function ◽  
And Control ◽  
Safe Set ◽  
Initial Results

This work presents the development of Adaptive Cruise Control (ACC) applied to a vehicle. The ACC tracks a predefined controlled vehicle cruise speed, however when a leading vehicle with lower speed is encountered, the ACC must adapt the controlled vehicle speed to maintain a safe distance between the vehicles. The control strategy applied combines Control Lyapunov Function (CLF), related to performance/stability objectives and Control Barrier Function (CBF), related to safety conditions represented by a safe set. CLF and CBF are integrated with Quadratic Programming (QP) and a relaxation is used to make performance/stability objectives as a soft constraint and safety conditions as a hard constraint. The system model is based on a vehicle available at EPUSP and presents an input time-delay, that can degrade performance and stability. The input delay is compensated with a Smith Predictor. The initial results were obtained through numerical simulations and, in the future, the scheme will be implemented in the vehicle. The numerical simulations indicate that the proposed controller respect the performance/stability objectives and the safety conditions.

scholarly journals Neural Network Optimized Model Predictive Multi-Objective Adaptive Cruise Control

2018 ◽  
Vol 166 ◽  
pp. 01009
Author(s):  
Siyi Zhang ◽  
Junzhi Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Computational Complexity ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Radar Sensor ◽  
Multi Objective ◽  
The Neural Network ◽  
Sensor Models ◽  
Optimized Model

A model predictive multi-objective adaptive cruise control (MPC MO-ACC) system, designed to consider both the tracking performance and the fuel consumption, is optimized by a neural network in this paper, reducing the computational complexity without sacrificing the control performance. The optimized MO-ACC control system is built by training a neural network with the control results of the MPC MO-ACC system. Simulation tests are conducted in Matlab/Simulink in conjunction with the high-fidelity CarMaker software. Influences of four driving conditions (the learning track, NEDC, JP05, FTP75) and two kinds of sensor models (ideal radar sensor and 77GHz physical radar sensor) are analysed. Simulation results have shown that the neural network optimized model predictive MO-ACC has the same control capability and strong robustness as the original MPC MO-ACC. Meanwhile, the optimized control system has much lower computational complexity, which shows potentials for the application in real-time vehicle control and industry.

Adaptive Cruise Control with a Customized Electronic Control Unit

2018 ◽  
Vol 30 (1) ◽  
pp. 9-15
Author(s):  
Mateus Mussi Brugnolli ◽  
Bruno Silva Pereira ◽  
Bruno Augusto Angélico ◽  
Armando Antônio Maria Laganá
Keyword(s):  
Adaptive Cruise Control ◽  
Electronic Control Unit ◽  
Control Unit ◽  
Electronic Control ◽  
Cruise Control

scholarly journals Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening

2020 ◽  
Vol 10 (5) ◽  
pp. 1635
Author(s):  
Lie Guo ◽  
Pingshu Ge ◽  
Dachuan Sun ◽  
Yanfu Qiao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Vehicle Performance ◽  
Car Following ◽  
Control Precision ◽  
Performance Indexes ◽  
Preceding Vehicle ◽  
And Control

In this paper, with the aim of meeting the requirements of car following, safety, comfort, and economy for adaptive cruise control (ACC) system, an ACC algorithm based on model predictive control (MPC) using constraints softening is proposed. A higher-order kinematics model is established based on the mutual longitudinal kinematics between the host vehicle and the preceding vehicle that considers the changing characteristics of the inter-distance, relative velocity, acceleration, and jerk of the host vehicle. Performance indexes are adopted to represent the multi-objective demands and constraints of the ACC system. To avoid the solution becoming unfeasible because of the overlarge feedback correction, the constraint softening method was introduced to improve robustness. Finally, the proposed ACC method is verified in typical car-following scenarios. Through comparisons and case studies, the proposed method can improve the robustness and control precision of the ACC system, while satisfying the demands of safety, comfort, and economy.

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