☆ 4.6 Article

Efficient Speed Planning for Autonomous Driving in Dynamic Environment With Interaction Point Model

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Civil

Trajectory Planning Based on Spatio-Temporal Map With Collision Avoidance Guaranteed by Safety Strip

Ting Zhang et al.

Summary: This paper proposes a trajectory planning framework based on spatio-temporal map to generate efficient and safety guaranteed trajectories in complex environments. The framework consists of rough search, fine optimization, and collision avoidance sections. Experiments demonstrate that the framework can adapt well to different types of complex traffic scenes.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

添加到收藏夹

Article Robotics

GamePlan: Game-Theoretic Multi-Agent Planning With Human Drivers at Intersections, Roundabouts, and Merging

Rohan Chandra et al.

Summary: This article presents a new method for multi-agent planning involving human drivers and autonomous vehicles in unsignaled intersections, roundabouts, and during merging. The method utilizes game theory to develop an auction-based approach that determines the optimal action for each agent based on their driving style, effectively preventing collisions and deadlocks.

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

添加到收藏夹

Article Robotics

Interactive Multi-Modal Motion Planning With Branch Model Predictive Control

Yuxiao Chen et al.

Summary: Motion planning for autonomous robots and vehicles in the presence of uncontrolled agents is a challenging problem. This article proposes a branch Model Predictive Control (MPC) framework that plans over feedback policies to leverage the reactive behavior of the uncontrolled agent, achieving a balance between safety and performance.

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

添加到收藏夹

Article Engineering, Civil

Helping Automated Vehicles With Left-Turn Maneuvers: A Game Theory-Based Decision Framework for Conflicting Maneuvers at Intersections

Yalda Rahmati et al.

Summary: The study aims to simulate human behavior at intersections more realistically by proposing a decision-making dynamic and extracting modeling data from CAVs' perception system. Through validation and sensitivity analysis, the proposed model demonstrates robustness to environmental uncertainties and accurately captures human drivers' real-world behavior in unprotected left-turn maneuvers.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

添加到收藏夹

Proceedings Paper Automation & Control Systems

TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

Ruochen Jiao et al.

Summary: This work proposes a behavior-aware Trajectory Autoencoder (TAE) model that explicitly models drivers' behavior to provide more information for trajectory generation and prediction, enhancing decision making in critical scenarios.

2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2022)

添加到收藏夹

Article Robotics

Fail-Safe Motion Planning for Online Verification of Autonomous Vehicles Using Convex Optimization

Christian Pek et al.

Summary: This article presents a verification technique for safe motion planning of autonomous vehicles, ensuring collision avoidance using fail-safe trajectories. The real-time capable verification technique operates under the premise of executing only verified safe trajectories, demonstrating potential to drastically reduce accidents without overly conservative behaviors.

IEEE TRANSACTIONS ON ROBOTICS (2021)

添加到收藏夹

Article Robotics

Game-Theoretic Planning for Self-Driving Cars in Multivehicle Competitive Scenarios

Mingyu Wang et al.

Summary: This article presents a nonlinear receding horizon game-theoretic planner specifically designed for autonomous cars in competitive scenarios, providing rich game strategies for trajectories through constraints and optimizations on trajectory and bicycle kinematics. The planner iteratively plans trajectories for the ego vehicle and other vehicles, incorporating a sensitivity term for collision avoidance and significantly outperforming a baseline planner in numerical simulations and experiments.

IEEE TRANSACTIONS ON ROBOTICS (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving

Alexander Cui et al.

Summary: This paper introduces a novel autonomy system LOOKOUT, which perceives the environment, predicts diverse future trajectories, and optimizes contingency plans for safe reactions to different scenarios. The model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset, as well as safer and less conservative motion plans in long-term closed-loop simulations compared to current state-of-the-art models.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

添加到收藏夹

Proceedings Paper Automation & Control Systems

Prediction-Based Reachability for Collision Avoidance in Autonomous Driving

Anjian Li et al.

Summary: The study proposes a prediction-based reachability framework for computing safety controllers in autonomous driving, which effectively reduces conservatism and collision risk between interacting vehicles.

2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) (2021)

添加到收藏夹

Proceedings Paper Automation & Control Systems

Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles

Josiah P. Hanna et al.

Summary: Recognizing the goals or intentions of observed vehicles is crucial for predicting the long-term behavior of agents in autonomous driving scenarios. The GOFI algorithm introduces probabilistic belief inference over goals and potential occluded factors, improving accuracy in recognizing true world states and enabling safe navigation in various scenarios.

2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2021)

添加到收藏夹

Article Engineering, Civil

Response Time and Time Headway of an Adaptive Cruise Control. An Empirical Characterization and Potential Impacts on Road Capacity

Michail Makridis et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

添加到收藏夹

Proceedings Paper Automation & Control Systems

Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching

Lu Zhang et al.

2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) (2020)

添加到收藏夹

Article Ergonomics

Defining interactions: a conceptual framework for understanding interactive behaviour in human and automated road traffic

G. Markkula et al.

THEORETICAL ISSUES IN ERGONOMICS SCIENCE (2020)

添加到收藏夹

Article Robotics

Safe Trajectory Generation for Complex Urban Environments Using Spatio-Temporal Semantic Corridor

Wenchao Ding et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction

Constantin Hubmann et al.

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (2018)

添加到收藏夹

Article Engineering, Civil

Combined Variable Speed Limit and Lane Change Control for Highway Traffic

Yihang Zhang et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2017)

添加到收藏夹

Article Engineering, Civil

A multilevel collision mitigation approach - Its situation assessment, decision making, and performance tradeoffs

Joerg Hillenbrand et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2006)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.