Graph-based Planning-informed Trajectory Prediction for Autonomous Driving

Dong, Qing; Jiang, Titong; Xu, Tao; Liu, Yahui

doi:10.1109/cvci56766.2022.9964966

Cited by 6 publications

(1 citation statement)

References 35 publications

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“…The sequential decision problems of lane changing and overtaking can be modeled as Markov decision processes, and reinforcement learning methods are applied to decision making [ 13 ]. Graph neural networks (GNNs) excel in handling complex traffic scenarios and have been integrated into the decision-making processes of intelligent agents [ 14 ]. In a recent study by [ 15 ], GNNs combined with Double Deep Q-learning networks demonstrated effective multi-vehicle decision making in dynamic scenarios.…”

Section: Related Workmentioning

confidence: 99%

Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization

Feng,

Wei,

Zhao

et al. 2024

Sensors

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This paper considers the interactive effects between the ego vehicle and other vehicles in a dynamic driving environment and proposes an autonomous vehicle lane-changing behavior decision-making and trajectory planning method based on graph convolutional networks (GCNs) and multi-segment polynomial curve optimization. Firstly, hierarchical modeling is applied to the dynamic driving environment, aggregating the dynamic interaction information of driving scenes in the form of graph-structured data. Graph convolutional neural networks are employed to process interaction information and generate ego vehicle’s driving behavior decision commands. Subsequently, collision-free drivable areas are constructed based on the dynamic driving scene information. An optimization-based multi-segment polynomial curve trajectory planning method is employed to solve the optimization model, obtaining collision-free motion trajectories satisfying dynamic constraints and efficiently completing the lane-changing behavior of the vehicle. Finally, simulation and on-road vehicle experiments are conducted for the proposed method. The experimental results demonstrate that the proposed method outperforms traditional decision-making and planning methods, exhibiting good robustness, real-time performance, and strong scenario generalization capabilities.

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Section: Related Workmentioning

confidence: 99%

Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization

Feng,

Wei,

Zhao

et al. 2024

Sensors

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Plan Future Graph Visualization and Improvement

Croft

2024

Communications in Computer and Information Science

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A Novel Lane-Change Decision-Making With Long-Time Trajectory Prediction for Autonomous Vehicle

Wang,

Hu,

Wei

et al. 2023

IEEE Access

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In the process of autonomous vehicle lane changing, a reliable decision-making system is crucial for driving safety and comfort. However, traditional decision-making systems have short-term characteristics, which makes them susceptible to real-time inference from surrounding vehicles. Usually, system sacrifices driving comfort to ensure the safety of the lane change. Balancing driving safety and comfort has always been a research challenge. Long-term trajectory prediction can provide accurate future trajectories of target vehicles, providing reliable long-term information to compensate for the short-term variability of decision systems. This paper proposes a novel decision-making model with long-term trajectory prediction for lane-changing. First, we constructed a long-term trajectory prediction model to predict the trajectories of surrounding vehicles. Besides, we built a lane change decision-making model based on fuzzy inferencing, considering the predicted trajectories to infer the relative relationship between other vehicles and the self-driving car. The establishment of the fuzzy rule library considered the vehicle speed, acceleration, system delay time, driver delay time and the distance between vehicles. Finally, we created a dataset for training and testing the trajectory prediction model, and we built 4 cases simulation environments, for two or three vehicles on a straight road or curved road, respectively, to test the decisionmaking model. Experimental results show that our proposed model can ensure driving safety and improve driving comfort.

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Graph-based Planning-informed Trajectory Prediction for Autonomous Driving

Cited by 6 publications

References 35 publications

Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization

Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization

Plan Future Graph Visualization and Improvement

A Novel Lane-Change Decision-Making With Long-Time Trajectory Prediction for Autonomous Vehicle

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