Path Tracking and Handling Stability Control Strategy With Collision Avoidance for the Autonomous Vehicle Under Extreme Conditions

Chen, Yong; Chen, Sizhong; Ren, Hongbin; Gao, Zepeng; Liu, Zheng

doi:10.1109/tvt.2020.3031661

Cited by 57 publications

(18 citation statements)

References 42 publications

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“…Hang et al 13 proposed a tube-based MPC approach which takes control vector constraints, lateral stability constraints, rollover prevention constraints, and path tracking error constraints into account. Chen et al 14 designed both path tracking controller and the stability controller with considering multi-constraints based on LTV-MPC and verified the controller in HIL simulation environment. Wurts et al 15 developed a collison immient steering system NMPC with advanced constraint aggregation techniques.…”

Section: Introductionmentioning

confidence: 99%

A cloud-edge combined control system with MPC parameter optimization for path tracking of unmanned ground vehicle

Yang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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As a new carrier to carry out intelligent task, the unmanned ground vehicle (UGV) is an important component of the intelligent transportation system (ITS) in the future. Most of traditional UGV path tracking control methods are deployed in the edge-side (on-board computing platform) with restricted data, which severely limits the improvement of UGVs’ path tracking performance. Therefore, this paper proposes a novel cloud-edge combined control system with MPC parameter optimization based on cloud brain control center (CBCC) applied in future ITS, which consists of edge-side control module and cloud-side optimization module. The proposed control system can optimize control parameters iteratively in CBCC by making the utmost of big data generated by UGVs to markedly enhance the control effectiveness. In CBCC, the path tracking performance is quantitatively evaluated from the aspects of path tracking accuracy, vehicle stability, and control stability. Also, an optimization algorithm is established by using SVR theory. Based on this, the cloud-side optimization module optimizes the control parameters of the edge-side control module by collecting and processing the big data generated by UGVs while driving. Besides, the designed edge-side control module is based on MPC algorithm and innovatively introduces a compensator which obviously reduces the error caused by the inaccuracy of the prediction model. To verify the effectiveness of the proposed cloud-edge combined control system, numerous simulation experiments are carried out. The results shows that the proposed cloud-edge combined control system can improve UGV’s path tracking performance and have strong robustness at different speeds.

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Section: Introductionmentioning

confidence: 99%

A cloud-edge combined control system with MPC parameter optimization for path tracking of unmanned ground vehicle

Yang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Currently, collision avoidance is becoming the standard associate system on most autonomous vehicle (AV) and is regarded as the most effective way to reduce traffic crash accidents, including frontal crashes, side crashes, rear-end crashes and so on [1][2].…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, unlike the works [1], [4], [7], [12] and [22] that investigate the collision avoidance by steering or braking separately, we propose an integrated collision avoidance strategy by simultaneously considering steering and braking. Although [2], [9], [24] proposed integrated collision avoidance by steering and braking, they do not investigate the rollover stability in emergency collision avoidance and how to coordinate the tracking performance and driving stability.…”

Section: Introductionmentioning

confidence: 99%

Emergency Collision Avoidance Strategy for Autonomous Vehicles Based on Steering and Differential Braking

Zheng

et al. 2022

Preprint

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This paper develops a novel integrated collision avoidance strategy for autonomous vehicles in emergency based on steering and braking. Specifically, the framework of the collision avoidance strategy is composed of two parts: an up-level decision-making layer and a low-level controller layer. The purpose of the up-level is to select the appropriate control strategy based on the vehicle information, and the low-level is to drive the vehicle according to the instructions generated by the up-level. More concretely, a novel control strategy is proposed by integrating with four-wheel steering, active rear steering and differential braking with guaranteed path-tracking accuracy and driving stability by adaptive model predictive control (AMPC). Finally, extensive co-simulations in MATLAB/Simulink and CarSim are conducted to verify the effectiveness of the proposed collision avoidance strategy in terms of tracking error, yaw rate and roll angle.

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“…At present, active obstacle avoidance is a standard associate system for intelligent vehicles and as a way to decrease traffic accidents [1][2]. For obstacle avoidance control, most researchers utilize a path planner and a tracking controller [3].…”

Section: Introductionmentioning

confidence: 99%

Active Obstacle Avoidance Control Strategy Based on Four-Wheel Steering

Xia

2022

J. Phys.: Conf. Ser.

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Active obstacle avoidance control strategy is the main question of intelligent vehicles, and active four-wheel steering is gradually used in the intelligent control system. Considering both tracking performance and driving characters, an active controller with four-wheel steering (4WS) and active rear steering (ARS) based on adaptive model predictive theory (AMPC) is designed. The control architecture is composed a supervisor and an AMPC controller. The supervisor is used to select the appropriate control mode and the AMPC is used to calculate the expected steering angle when the stability indexes over the safety threshold. Finally, the proposed control strategy is simulated via Carsim-Simulink co-simulation. The results show that the integrated controller can track the obstacle avoidance path and has good driving performance.

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Path Tracking and Handling Stability Control Strategy With Collision Avoidance for the Autonomous Vehicle Under Extreme Conditions

Cited by 57 publications

References 42 publications

A cloud-edge combined control system with MPC parameter optimization for path tracking of unmanned ground vehicle

A cloud-edge combined control system with MPC parameter optimization for path tracking of unmanned ground vehicle

Emergency Collision Avoidance Strategy for Autonomous Vehicles Based on Steering and Differential Braking

Active Obstacle Avoidance Control Strategy Based on Four-Wheel Steering

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