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Article
Engineering, Civil
Zhiqiang Jian et al.
Summary: This article analyzes the issues with current path planning frameworks in autonomous driving systems and proposes a global path planning framework based on multiple models. The framework divides the planning process into layers and adaptsively adjusts the process according to changes in traffic scenes, ensuring safety and flexibility in driving.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Robotics
Wenchao Ding et al.
Summary: EPSILON is an efficient interaction-aware planning system for automated driving, with a hierarchical structure that achieves human-like driving behaviors in highly interactive traffic flow smoothly and safely without being overconservative compared to existing planning methods.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Robotics
Zhongqiang Ren et al.
Summary: Path planning among dynamic obstacles is a fundamental problem in Robotics. In this work, we propose an algorithm called MO-SIPP that efficiently solves the problem of Multi-Objective Path Planning with Dynamic Obstacles by combining the ideas from SIPP and multi-objective A* algorithms.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Yingbing Chen et al.
Summary: This paper proposes a learning-based Interaction Point Model (IPM) to describe the interaction between agents in autonomous driving, and integrates the model into a planning framework, demonstrating its effectiveness and robustness through comprehensive simulations in highly dynamic environments.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Proceedings Paper
Automation & Control Systems
Wenda Xu et al.
Summary: This paper presents a novel convex optimization approach to address the minimum-time speed planning problem with dynamic obstacle constraints and point-wise speed and acceleration constraints. The contributions of this paper are three-fold: formulating the speed planning as an iterative convex optimization problem, proposing a modified vertical cell decomposition method to handle dynamic obstacles, and demonstrating significant improvement over previous work in typical driving scenarios.
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022)
(2022)
Proceedings Paper
Automation & Control Systems
Yutaka Shimizu et al.
Summary: In this paper, a linear programming (LP) based velocity planning method for self-driving vehicles is proposed, considering jerk limit and obstacle avoidance constraints. The proposed method is compared with other optimization-based approaches and shown to generate a velocity profile that satisfies the requirements more efficiently. Additionally, the effectiveness of the proposed method is validated through testing on a real vehicle.
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022)
(2022)
Article
Robotics
Jinyun Zhou et al.
Summary: The research introduces a free space trajectory optimization algorithm for autonomous driving, which outperforms other existing algorithms in terms of collision avoidance, control feasibility, and computation efficiency. It also meets stricter driving comfort standards in complex driving scenarios.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhejun Zhang et al.
Summary: This paper introduces a method for training end-to-end driving agents supervised by a reinforcement learning expert, which effectively improves driving performance and learning efficiency, as well as the generalization ability under different town and weather conditions.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Stefano Albrecht et al.
Summary: The proposed integrated prediction and planning system for autonomous driving utilizes rational inverse planning to recognize other vehicles' goals, enabling an optimal maneuver planning for the ego vehicle through a Monte Carlo Tree Search (MCTS) algorithm. The system demonstrates the ability to reduce driving times significantly in urban driving scenarios by robustly recognizing other vehicles' goals, with explanations for the predictions based on rationality principles.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Lu Zhang et al.
2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2020)
Article
Computer Science, Software Engineering
Bartolomeo Stellato et al.
MATHEMATICAL PROGRAMMING COMPUTATION
(2020)
Article
Automation & Control Systems
Shlomi Hacohen et al.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2019)
Article
Robotics
Wenchao Ding et al.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2019)
Article
Robotics
Hai Zhu et al.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2019)
Article
Robotics
Lars Blackmore et al.
IEEE TRANSACTIONS ON ROBOTICS
(2011)
