IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

Chen, Yingbing; Cheng, Jie; Gan, Lu; Wang, Sheng; Liu, Hongji; Mei, Xiaodong; Liu, Ming

doi:10.1109/tits.2024.3360440

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2024

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Cited by 3 publications

References 32 publications

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Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions

Wang,

Chen,

Cheng

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions

Wang,

Chen,

Cheng

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

Yao,

Zhang,

Sun

et al. 2024

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Leveraging Cooperative Intent and Actuator Constraints for Safe Trajectory Planning of Autonomous Vehicles in Uncertain Traffic Scenarios

Zhu,

Lv,

Liu

2024

Actuators

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This study explores the integration of dynamic vehicle trajectories, vehicle safety factors, static traffic environments, and actuator constraints to improve cooperative intent modeling for autonomous vehicles (AVs) navigating uncertain traffic scenarios. Existing models often focus solely on interactions between dynamic trajectories, limiting their ability to fully interpret the intentions of surrounding vehicles. To address this limitation, we present a more comprehensive approach using the Cooperative Intent Multi-Layer Graph Neural Network (CMGNN) model. The CMGNN analyzes not only the dynamic trajectories but also the lane position relationships, vehicle angle changes, and actuator constraints and performs group interaction analysis. This richer information allows the CMGNN to more accurately capture the cooperative intent and better understand the surrounding vehicle behavior. This study investigated the impact of the CMGNN in the Carla simulator on surrounding vehicle trajectory prediction and AV safe trajectory planning. An innovative mechanism for dynamic trajectory risk assessment is introduced, which takes into account the constraints of the actuators when evaluating trajectory planning metrics. The results show that incorporating cooperative intent and considering the actuator limitations enhanced the CMGNN’s safety and driving efficiency in uncertain scenarios, significantly reducing the probability of AVs colliding. This is achieved as the model dynamically adapts its driving strategy based on the real-time traffic conditions, the perceived intentions of the surrounding vehicles, and the physical constraints of the vehicle actuators.

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IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

Cited by 3 publications

References 32 publications

Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions

Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions

MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

Leveraging Cooperative Intent and Actuator Constraints for Safe Trajectory Planning of Autonomous Vehicles in Uncertain Traffic Scenarios

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