Potential field–based hierarchical adaptive cruise control for semi-autonomous electric vehicle

2018 ◽  
Vol 233 (10) ◽  
pp. 2479-2491 ◽  
Author(s):  
Yue Ren ◽  
Ling Zheng ◽  
Wei Yang ◽  
Yinong Li
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Potential Field ◽  
Adaptive Cruise Control ◽  
Ride Comfort ◽  
Tracking Error ◽  
Driving Safety ◽  
Vehicle Speed ◽  
Cruise Control ◽  
Collision Risk

Adaptive cruise control, as a driver assistant system for vehicles, can adjust the vehicle speed to keep the appropriate distance from other vehicles, which highly increases the driving safety and driver’s comfort. This paper presents hierarchical adaptive cruise control system that could balance the driver’s expectation, collision risk, and ride comfort. In the adaptive cruise control structure, there are two controllers to achieve the function. The one is the upper controller which is established based on the model predictive control theory and used to calculate the desirable longitudinal acceleration. The collision risk is described by the Gaussian distribution. A quadratic cost function for model predictive control is formulated based on the potential field method through the contradictions between the tracking error, collision risk, and the longitudinal ride comfort. The other one is the lower optimal torque vectoring controller which is constructed based on the vehicle longitudinal dynamics. And it can generate the desired acceleration considering the anti-wheel slip limitations. Several simulations under different road conditions demonstrate that the proposed adaptive cruise control has significant performance on balancing the tracking ability, collision avoidance, ride comfort, and adhesion utilization. It also maintains vehicle stability for the complex road conditions.

scholarly journals Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 5293
Author(s):  
Chongpu Chen ◽  
Jianhua Guo ◽  
Chong Guo ◽  
Chaoyi Chen ◽  
Yao Zhang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Finite State Machine ◽  
Adaptive Cruise Control ◽  
State Machine ◽  
Driving Safety ◽  
Cruise Control ◽  
Finite State ◽  
Distance Model ◽  
Model Predictive Control Algorithm

In a cut-in scenario, traditional adaptive cruise control usually cannot effectively identify the cut-in vehicle and respond to it in advance. This paper proposes an adaptive cruise control (ACC) strategy based on the MPC algorithm for cut-in scenarios. A finite state machine (FSM) is designed to manage vehicle control in different cut-in scenarios. For a cut-in scenario, a method to identify and quantify the possibility of cut-in of a vehicle is proposed. At the same time, a safety distance model of the cut-in vehicle is established as the basis for the state transition of the finite state machine. Taking the quantified cut-in possibility of a vehicle as a reference, the model predictive control (MPC) algorithm, which considers the constraints of driving safety and comfort, is used to realize coordinated control of the host vehicle and the cut-in vehicle. Simulink–Carsim simulation studies show that the ACC strategy for a cut-in scenario can effectively identify a cut-in vehicle and quantify the possibility of cut-in of the vehicle. Faced with a cut-in vehicle, the host vehicle using the ACC strategy can brake several seconds early and switch the following target to the cut-in vehicle. Meanwhile, the acceleration and jerk of the host vehicle changes within a reasonable range, which ensures driving safety and comfort.

scholarly journals Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm

2020 ◽  
Vol 10 (15) ◽  
pp. 5271
Author(s):  
Zifei Nie ◽  
Hooman Farzaneh
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Car Following ◽  
Preceding Vehicle ◽  
Variation Rate ◽  
Model Predictive Control Algorithm

An adaptive cruise control (ACC) system is developed based on eco-driving for two typical car-following traffic scenes. The ACC system is designed using the model predictive control (MPC) algorithm, to obtain objectives of eco-driving, driving safety, comfortability, and tracking capability. The optimization of driving comfortability and the minimization of fuel consumption are realized in the manner of constraining the acceleration value and its variation rate, so-called the jerk, of the host vehicle. The driving safety is guaranteed by restricting the vehicle spacing always larger than minimum safe spacing from the host vehicle to the preceding vehicle. The performances of the proposed MPC-based ACC system are evaluated and compared with the conventional proportional-integral-derivative (PID) controller-based ACC system in two representative driving scenarios, through a simulation bench and an instantaneous emissions and fuel consumption model. In addition to meeting the other driving objectives mentioned above, the simulation results indicate an improvement of 13% (at the maximum) for fuel economy, which directly shows the effectiveness of the presented MPC-based ACC system.

scholarly journals Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction

2019 ◽  
Vol 9 (22) ◽  
pp. 4875 ◽  
Author(s):  
Hanwool Woo ◽  
Hirokazu Madokoro ◽  
Kazuhito Sato ◽  
Yusuke Tamura ◽  
Atsushi Yamashita ◽  
...  
Keyword(s):  
Adaptive Cruise Control ◽  
Individual Characteristics ◽  
Driving Safety ◽  
Cruise Control ◽  
Collision Risk ◽  
Trajectory Prediction ◽  
Lane Changes ◽  
The Individual ◽  
Near Future ◽  
Real Traffic

In this paper, we propose an advanced adaptive cruise control to evaluate the collision risk between adjacent vehicles and adjust the distance between them seeking to improve driving safety. As a solution for preventing crashes, an autopilot vehicle has been considered. In the near future, the technique to forecast dangerous situations and automatically adjust the speed to prevent a collision can be implemented to a real vehicle. We have attempted to realize the technique to predict the future positions of adjacent vehicles. Several previous studies have investigated similar approaches; however, these studies ignored the individual characteristics of drivers and changes in driving conditions, even though the prediction performance largely depends on these characteristics. The proposed method allows estimating the operation characteristics of each driver and applying the estimated results to obtain the trajectory prediction. Then, the collision risk is evaluated based on such prediction. A novel advanced adaptive cruise control, proposed in this paper, adjusts its speed and distance from adjacent vehicles accordingly to minimize the collision risk in advance. In evaluation using real traffic data, the proposed method detected lane changes with 99.2% and achieved trajectory prediction error of 0.065 m, on average. In addition, it was demonstrated that almost 35% of the collision risk can be decreased by applying the proposed method compared to that of human drivers.

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