Self-Adaptive Motion Prediction-Based Proactive Motion Planning for Autonomous Driving in Urban Environments

Jeong, Yonghwan

doi:10.1109/access.2021.3100590

Cited by 9 publications

(6 citation statements)

References 52 publications

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“…However, they all centered on an extremely low-speed driving scene where the vehicle drives under 10 m/s and discussed less on the path. Recently, Wang et al [24] presented an occlusion-aware motion planning scheme that computed a static game tree considering the potential risk probability of the occluded area. But the research only validated the effectiveness of the proposed method within the vehicle kinematic under low-speed conditions.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

Lin

Tsukada

2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.

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Section: Related Workmentioning

confidence: 99%

Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

Lin

Tsukada

2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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“…As mentioned before, the reference states should be defined within the prediction horizon of the MPC. This means that the state prediction of the vehicle should be performed in order to define the reference states [10,[52][53][54]58,59]. First, the state and input vectors of the motion prediction model is defined as (11).…”

Section: Reference State Decisionmentioning

confidence: 99%

“…It is necessary to determine the longitudinal and lateral behaviors, which occur after the virtual inputs are applied, with the consideration of the actuation delay. If the virtual input is directly integrated by the prediction model without any considerations, the generated reference state can be the states that the actual vehicle cannot follow [59]. In this paper, the first-order delay model is used to describe the actuation delay in the prediction model.…”

Section: Reference State Decisionmentioning

confidence: 99%

“…The control algorithm for the over-actuated vehicle with 4WIS/4WID is designed with MPC. MPC has the advantage that an optimization has been performed for each moment while keeping future behavior in the account [9,10,[48][49][50][51][52]59]. This advantage is realized by performing the optimization for a finite horizon and then optimizing repeatedly for every sampling time of the controller.…”

Section: Design Of Linear Time-varying Model Predictive Controllermentioning

confidence: 99%

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Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Jeong

Yim

2021

Electronics

Self Cite

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This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to the multiple actuators. The proposed algorithm has two modules: reference state decision and MPC-based vehicle motion controller. Reference state decision module determines reference state profiles consisting of yaw rate and velocity in order to overcome the limitation of the error dynamics-based path tracking controller, which requires several assumptions on the reference path. The MPC-based vehicle motion controller is designed with a linear time-varying vehicle model in order to optimally allocate the control effort to each actuator. A linear time-varying MPC is adopted to reduce computational burden caused by using a non-linear one. The effectiveness of the proposed algorithm is validated via simulation on MATLAB/Simulink and CarSim. The simulation results show that the proposed algorithm improves the reference tracking performance by effectively distributing the control effort to the steering angle and driving force of each actuator.

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“…In view of the lack of accuracy of vehicle state parameters, many scholars optimize the vehicle state parameters through multi-algorithm fusion to obtain more accurate parameter values [12][13][14][15] . Chen et al [16] proposed a sliding mode control method based on radial basis neural network in order to obtain the accurate front wheel angle of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Research on trajectory tracking control of unmanned vehicle based on PSO_PID

Zheng,

2024

Preprint

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To enhance the tracking accuracy and stability of unmanned vehicles in trajectory tracking control, a particle swarm optimized proportional-integral-derivative (PID) control mixed with model predictive control is established. First, the model predictive controller is generated based on the vehicle dynamics model to calculate the lateral error; secondly, the particle swarm optimized PID controller is used to compensate the front wheel angle to reduce the steady-state lateral error; finally, the joint simulation analysis is carried out. The results of the study show that the MPC control incorporating PSO_PID exhibits a significant improvement in the lateral position error compared to the conventional MPC control, reducing the error by 65.51% and effectively improving the tracking accuracy. In addition, the method also successfully reduces the peak yaw angle error by 57.89% and the peak fluctuation amplitude of the vehicle's front wheel angle by 16.33%, thus significantly improving the stability of trajectory tracking.

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Self-Adaptive Motion Prediction-Based Proactive Motion Planning for Autonomous Driving in Urban Environments

Cited by 9 publications

References 52 publications

Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Research on trajectory tracking control of unmanned vehicle based on PSO_PID

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