A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles

Peng, Tao; Lili, Su; Zhang, Ronghui; Guan, Zhiwei; Zhao, Huimin; Qiu, Zhijun; Zong, Changfu; Xu, Hang

doi:10.1016/j.eswa.2019.112953

Cited by 52 publications

(21 citation statements)

References 41 publications

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“…A dynamic tracking model (DTM) [19] has been designed to give the covariance matrix of state noise to compensate for the shortcomings of the KCF algorithm. Feedback linearization [20] combined with a decision mechanism is also used to control the tracking process. Finally, on the basis of integrated probabilistic data association (IPDA), modified smoothing IPDA (MSIPDA) [21] improves the fixed-lag smooth data association to improve tracking performance.…”

Section: Tracking Methodsmentioning

confidence: 99%

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

Zhao

Zhou

et al. 2020

Sensors

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The main task for real-time vehicle tracking is establishing associations with objects in consecutive frames. After occlusion occurs between vehicles during the tracking process, the vehicle is given a new ID when it is tracked again. In this study, a novel method to track vehicles between video frames was constructed. This method was applied on driving recorder sensors. The neural network model was trained by YOLO v3 and the system collects video of vehicles on the road using a driving data recorder (DDR). We used the modified Deep SORT algorithm with a Kalman filter to predict the position of the vehicles and to calculate the Mahalanobis, cosine, and Euclidean distances. Appearance metrics were incorporated into the cosine distances. The experiments proved that our algorithm can effectively reduce the number of ID switches by 29.95% on the model trained on the BDD100K dataset, and it can reduce the number of ID switches by 32.16% on the model trained on the COCO dataset.

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Section: Tracking Methodsmentioning

confidence: 99%

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

Zhao

Zhou

et al. 2020

Sensors

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“…Ref. [16] proposed a lanechange trajectory model based on Gaussian distribution, which used pure steering and combined braking. Moreover, a new trajectory tracking controller combined with a decision mechanism was designed using feedback linearization.…”

Section: Introductionmentioning

confidence: 99%

Investigation on hierarchical control for driving stability and safety of intelligent HEV during car-following and lane-change process

Yazhou

Wang

Ding

et al. 2021

Sci. China Technol. Sci.

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The longitudinal and lateral coordinated control for autonomous vehicles is fundamental to achieve safe and comfortable driving performance. Aiming at this for hybrid electric vehicles (HEV) during the car-following (CF) and lane-change (LC) process while accelerating, a hierarchical control strategy for vehicle stability control is proposed. This new approach is different from the conventional hierarchical control. On the basis of model predictive control (MPC) theory, a two-layer MPC controller is designed at the top level of the control structure. The upper layer is a linear time-varying MPC (LTV-MPC), while the lower layer is a hybrid MPC (HMPC). For the LTV-MPC controller, a control-oriented linear discrete model for HEV is established, which integrates the dynamic model with three degrees of freedom (DOF) and the car-following model. The lower-layer HMPC controller is designed on the basis of the analysis for HEV hybrid characteristics and the modelling for the mixed logic dynamic (MLD) model of the HEV powertrain. As for the bottom level, a control plant including the HEV powertrain model and the 7 DOF nonlinear dynamics of the vehicle body is established. In addition, the system stability is proven. A deep fusion of vehicle dynamics control and energy management is achieved. Compared with LC-ACC control and conventional ACC control, the simulation and the hardware-in-the-loop (HIL) test results under different driving scenarios show that the proposed hierarchical control strategy can effectively maintain lateral stability and safety under severe driving conditions. Additionally, the HEV powertrain output torque and the gear-shift point are coordinated and controlled by the HMPC controller.

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“…Whether advanced assisted driving or automatic driving, active collision avoidance is one of the main contents. 5–7…”

Section: Introductionmentioning

confidence: 99%

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

Zhang

Wang

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

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A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles

Cited by 52 publications

References 41 publications

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

Investigation on hierarchical control for driving stability and safety of intelligent HEV during car-following and lane-change process

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

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