A Novel Robust Lane Change Trajectory Planning Method for Autonomous Vehicle

Zeng, Dequan; Yu, Zhuoping; Liu, Xiong; Zhao, Junqiao; Zhang, Peizhi; Li, Zhiqiang; Fu, Zhiqiang; Yao, Jie; Zhou, Yi

doi:10.1109/ivs.2019.8814151

Cited by 18 publications

(7 citation statements)

References 28 publications

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“…Therefore, a global optimal solution cannot always be found in real-time [7]. The second category is sampling-based methods [16]. These techniques can generate many additional candidate paths that are not feasible or do not fulfill the safety constraints when navigating in close interaction with pedestrians.…”

Section: Channel-based Vehicle Maneuveringmentioning

confidence: 99%

Proactive And Smooth Maneuvering For Navigation Around Pedestrians

Kabtoul

Spalanzani

Martinet

2022

2022 International Conference on Robotics and Automation (ICRA)

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Navigation in close proximity with pedestrians is a challenge on the way to fully automated vehicles. Pedestrianfriendly navigation requires an understanding of pedestrian reaction and intention. Merely safety based reactive systems can lead to sub-optimal navigation solutions resulting in the freezing of the vehicle in many scenarios. Moreover, a strictly reactive method can produce unnatural driving patterns which cannot guarantee the legibility or social acceptance of the automated vehicle. This work presents a proactive maneuvering method adapted to navigation in close interaction with pedestrians using a dynamic channel approach. The method allows to proactively explore the navigation options based on anticipating pedestrians cooperation. The navigation is tested in frontal and lateral crossing scenarios with variable space density. The system is implemented under ROS, and compared with the probabilistic Risk-RRT planning method. The results are evaluated based on the safety and comfort of the pedestrians, and the quality of the vehicle's trajectory.

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Section: Channel-based Vehicle Maneuveringmentioning

confidence: 99%

Proactive And Smooth Maneuvering For Navigation Around Pedestrians

Kabtoul

Spalanzani

Martinet

2022

2022 International Conference on Robotics and Automation (ICRA)

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“…Schnelle et al [27] took the hyperbolic tangent function to generate the desired lane-changing path in a personalized driver model. Zeng et al [28] employed the parameterized cubic B-spline curve to plan a continuous curvature path. Based on the control points determined in advance, the generated trajectory could avoid potential collisions with surrounding vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

QPNet: Lane‐changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

Wang

Wei

2022

IET Intelligent Trans Sys

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Lane-changing is a basic driving behaviour, which largely impacts on traffic safety and efficiency. With the development of technology, the automated lane-changing system has attracted extensive attention. Among it, the trajectory planning part is a challenging problem owing to the complexity and diversity of the driving situations. The planner requires the real-time capability to produce safe and comfortable trajectories for coping with the dynamically changing environment. Based on this, the paper proposes a lane-changing trajectory planning model in dynamic driving environments. The model constructs a neural network to predict the end position of the ego vehicle, and then adopts the mathematical programming method to solve the optimal lane-changing trajectory that guarantees the ride safety and comfort. With the assistance of the neural network, the lane-changing trajectory planning problem is converted into a quadratic programming (QP) model, thereby achieving rapid solution of the model. Moreover, to train the proposed algorithm, a novel approach for generating the scenario data is designed, which can generate rich and diverse traffic scenarios at a low cost. The simulation results show that the proposed model can plan a lane-changing trajectory quickly and effectively, and the ego vehicle can follow the planned trajectory safely and comfortably.

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“…The cubic B-spline curve is employed to generate lane changing path for curvature continuous. The maximum curvature of the path is [39]- [41],…”

Section: Dbo Trajectory Planer a Dbo Path Planer 1) Lane Changingmentioning

confidence: 99%

“…As shown in Fig.4, the supplementary constraints from start segments to target segments are as follows [40], [41]:…”

Section: ) Turnmentioning

confidence: 99%

$DBO$ Trajectory Planning and $HAHP$ Decision-Making for Autonomous Vehicle Driving on Urban Environment

Zeng

Liu

et al. 2019

IEEE Access

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A novel driving behaviour oriented (DBO) trajectory planner and hierarchical analytic hierarchy process (HAHP) decision maker are presented for intelligent vehicle. Since driving on structural road should satisfy actuator constraints and improve comfortableness as soon as possible, which strictly obeys traffic rules other than making traffic mess, it is rather than purely pursuing the shortest route/time. By analysis traffic rules, theDBO framework is employed to produce trajectories. To make trajectory drivable, cubic B-spline and clothoid curve are modeled to keep continuous curvature, and cubic polynomial curve is to schedule velocity profile satisfying stability and comfort. To pick out the best trajectory, HAHP decision maker is developed to evaluate the candidates. The first layer selects optimal paths considering smoothness and economy, and the second layer selects best trajectory taking smoothness, comfortableness and economy in account. Moreover, DBO rapidly exploring random tree (RRT) replanner is embedded to ensure algorithm completeness. Finally, several typical scenarios are designed to verify the real-time and reliability of the algorithm. The results illustrate that the algorithm has highly real-time and stability evaluated by Statistical Process Control method as the probability for the peak time less than 0.1s is 100% except three obstacles avoidance scenario is 59.31% in 1000 cycles. Since the planned trajectory is smooth enough and satisfy the constraints of the actuator, the mean lateral tracking error is less than 0.2m with 0.5m peak error, and the mean speed error less than 0.5km/h with 1.5km/h peak error for all scenarios.

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A Novel Robust Lane Change Trajectory Planning Method for Autonomous Vehicle

Cited by 18 publications

References 28 publications

Proactive And Smooth Maneuvering For Navigation Around Pedestrians

Proactive And Smooth Maneuvering For Navigation Around Pedestrians

QPNet: Lane‐changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

$DBO$ Trajectory Planning and $HAHP$ Decision-Making for Autonomous Vehicle Driving on Urban Environment

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