Crash Mitigation in Motion Planning for Autonomous Vehicles

Wang, Hong; Huang, Yanjun; Khajepour, Amir; Zhang, Yubiao; Rasekhipour, Yadollah; Cao, Dongpu

doi:10.1109/tits.2018.2873921

Cited by 201 publications

(92 citation statements)

References 28 publications

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“…However, if the constructions of the rules and membership functions are very complicated, the empirical approach is not only time consuming and labor intensive, but also cannot ensure that the optimum rules and the membership functions are found [48,49]. In order to achieve a model that can tolerate imprecision and has controller parameters that are easy to set at the same time, model predictive control (MPC) [50,51] is one of promising options. To achieve the desired performance during high-speed driving, the MPC controller architecture considers both the kinematic and the dynamic control in a cascade structure [52].…”

Section: Related Workmentioning

confidence: 99%

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

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Section: Related Workmentioning

confidence: 99%

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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“…The artificial potential field approach is a typical numerical optimization algorithm that constructs the gravitational and the repulsion fields in order to guide the vehicle to the destination avoiding collision [13]. Ji and Wang et al superimposed the trigonometric function of the road and the exponential function of the obstacle, constructed a 3D virtual danger potential field, generated the ideal collision avoidance trajectory, and used the multi-constraint model predictive control (MMPC) to track the collision avoidance trajectory [14][15][16]. With the rapid development of artificial intelligence, machine learning is now being used to make decisions on autonomous vehicles.…”

Section: Related Researchmentioning

confidence: 99%

Anti-rollover Motion Planning for Automated Heavy Trucks

Jin

Wang

et al. 2020

Preprint

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Anti-rollover is an important performance for automated heavy trucks, which has been seldomly considered in the motion planning. This paper proposes an anti-rollover motion planning based on model predictive control (MPC) for automated heavy trucks. Taking the coupling of roll motion of sprung mass of the front axle with that of the drive axle into consideration, a seven degrees of freedom rollover dynamics model is established, and an evaluation index that can accurately describe the rollover motion is derived for heavy trucks. Then, a model predictive control strategy is designed for motion planning that combines the rollover dynamics, the artificial potential field for obstacle avoidance, and the trajectory tracking. In addition, the optimal path is calculated that considers collision avoidance, anti-rollover and vehicle dynamic constraints. Furthermore, three typical scenarios are applied to validate the performance of the proposed motion planning algorithm. The obtained results demonstrate that the proposed anti-rollover motion planning can effectively avoid collisions and reduce the rollover risk simultaneously when confronting edge scenarios.

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“…By predicting the nearby vehicle trajectory, the potential field of the nearby vehicle is constructed and considered in the controller design, in order to avoid possible collisions between the two vehicles. The potential field of the nearby vehicle can be written as [28] ( , )…”

Section: B Potential Fieldsmentioning

confidence: 99%

Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control

Chen

Zhang

et al. 2020

Preprint

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The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the lane-exchanging scenario. The nearby vehicle trajectory needs to be predicted, from which the autonomous vehicle is controlled to prevent possible collisions. This paper proposes a lane-exchanging driving strategy for the autonomous vehicle to cooperate with the nearby vehicle by integrating vehicle trajectory prediction and motion control. A trajectory prediction method is developed to anticipate the nearby vehicle trajectory. The Gaussian mixture model (GMM), together with the vehicle kinematic model, are synthesized to predict the nearby vehicle trajectory. A potential-field-based model predictive control (MPC) approach is utilized by the autonomous vehicle to conduct the lane-exchanging maneuver. The potential field of the nearby vehicle is considered in the controller design for collision avoidance. On-road driving data verification shows the nearby vehicle trajectory can be predicted by the proposed method. CarSim simulations validate that the autonomous vehicle can perform the lane-exchanging maneuver and avoid the nearby vehicle using the proposed driving strategy. The autonomous vehicle can thus safely perform the lane-exchanging maneuver and avoid the nearby vehicle.

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Crash Mitigation in Motion Planning for Autonomous Vehicles

Cited by 201 publications

References 28 publications

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Anti-rollover Motion Planning for Automated Heavy Trucks

Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control

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