Adaptive cruise control method based on hierarchical control and multi-objective optimization

Piao, Changhao; Gao, Jun; Yang, Qing; Shi, Junren

doi:10.1177/01423312221137508

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…ACC automatically can adjust its velocity along with the driving conditions of the preceding vehicle. Currently, the multi-objective coordinated ACC strategy, which integrates safety, fuel economy, and comfort, has been introduced [33]. Yet, owing to the unpredictable driving conditions and the movement status of surrounding vehicles, the ACC strategy suffers from frequent acceleration, deceleration, and unstable velocity control.…”

Section: Acc Framework Definitionmentioning

confidence: 99%

Research on a Multimode Adaptive Cruise Control Strategy with Emergency Lane-Changing Function

Dong

Yang

et al. 2023

WEVJ

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In emergency situations, it is difficult to meet the requirements of safe driving only by relying on the braking system, and the probability of accidents can be reduced by employing an emergency lane-changing mode. To improve the adaptability of the distributed electric vehicle adaptive cruise control (ACC) strategy to complicated and volatile conditions, a multimode ACC strategy with emergency lane-changing function is proposed. Firstly, the ACC is divided into four modes aimed at the problem of complex conditions, and a switching strategy is designed to control the switching of them. Simultaneously, the car-following mode is divided in greater detail based on time to collision (TTC), and the acceleration weighted average algorithm is adopted for accuracy and output continuity during switching. Then, the ACC is established with a hierarchical control framework, in which a PID-based cruise mode and a multi-objective optimized car-following mode based on model predictive control (MPC) are devised. The target brake wheel cylinder pressure is selected as the emergency brake pressure in takeover mode. In addition to the MPC-based system, the emergency lane-changing mode incorporates a yaw moment controller in the upper-level controller to improve body stability during emergency lane changing in the upper-level controller. In the lower-level controller, the upper-level output is converted into driving torque, wheel cylinder pressure, and front wheel angle to control vehicle travel and generate additional yaw moment. Finally, the results indicate that the presented multimode switching strategy can adapt to complex and instable transportation environments. In the cruise control scenario, the host vehicle can rapidly reach cruising speed within 5 s. In the car-following scenario, the host vehicle can stably follow the preceding vehicle with an acceleration of −5–3.5 m/s2 and a jerk of −2–2 m/s3 throughout the entire process, maintaining a safe distance from the preceding vehicle. In emergency lane-changing scenarios, vehicles with body stability control can better follow the lane-changing trajectory, and tracking accuracy is improved by 65%. Simultaneously, parameters such as front wheel angle, yaw rate, sideslip rate, and lateral acceleration remain within the normal range. In mixed switching scenarios, each mode can be correctly switched according to diverse operating conditions, and obstacle avoidance can be accomplished through horizontal and vertical strategies, which also verify the effectiveness and rationality of the control strategy proposed.

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Section: Acc Framework Definitionmentioning

confidence: 99%

Research on a Multimode Adaptive Cruise Control Strategy with Emergency Lane-Changing Function

Dong

Yang

et al. 2023

WEVJ

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“…Smart cars have great potential in reducing traffic accidents, alleviating traffic congestion, and improving road and vehicle utilization. Their main technologies can be divided into three parts: environmental perception [ 1 ], path planning [ 2 ], and decision-making control [ 3 ]. As a basic work in the environment perception of intelligent vehicles, multitarget tracking [ 4 ] is of great significance for ensuring the safety of autonomous vehicles and improving the ability of intelligent vehicles to understand the environment.…”

Section: Introductionmentioning

confidence: 99%

Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles

Shi

Tang

Gao

et al. 2023

Sensors

Self Cite

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Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker.

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“…In the actual driving process, the control algorithm of ACC system needs to coordinate various performance indicators. Piao et al [12] proposed an ACC method based on multi-objective real-time optimization control algorithm, which could improve the economy under the premise of ensuring comfort and vehicle following. Although many studies have improved the following, safety, comfort, and economy of the ACC system, there is a problem which is how to solve the contradictions between various performance indicators.…”

Section: Introductionmentioning

confidence: 99%

Research on Curving Acc System for Electric Vehicle Driven by In‐wheel Motors Based on Fmpc And Smc

Yang,

Tian

2023

Advcd Theory and Sims

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In order to ensure the lateral stability of adaptive cruise control (ACC) vehicles during curve following, this paper proposes a hierarchical multi‐objective coordinated control ACC system structure for in‐wheel motor drive electric vehicles. Different control strategies are designed for the cruise mode and car‐following mode of the ACC system. A proportion integral differential (PID) controller is used to meet the need for speed control in cruise mode. For the car‐following mode, a multi‐objective ACC system distance control strategy is proposed considering the performance of car following, passenger comfort, driving safety and driving economy of ACC vehicles. The weight coefficients in the objective function are updated in real time by using the fuzzy model predictive control (FMPC) strategy to meet the needs of different road conditions and improve the driving economy. Then, the desired front wheel steering angle is obtained by preview control theory. According to the theory of sliding mode control (SMC), a lateral stability controller is designed to track the desired yaw rate and sideslip angle. Simulation results show that the electric vehicle driven by in‐wheel motors based on FMPC and SMC has better car‐following performance and lateral stability performance under variable curvature curved road conditions.

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Adaptive cruise control method based on hierarchical control and multi-objective optimization

Cited by 4 publications

References 25 publications

Research on a Multimode Adaptive Cruise Control Strategy with Emergency Lane-Changing Function

Research on a Multimode Adaptive Cruise Control Strategy with Emergency Lane-Changing Function

Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles

Research on Curving Acc System for Electric Vehicle Driven by In‐wheel Motors Based on Fmpc And Smc

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