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, Electrical & Electronic
Letian Gao et al.
Summary: This paper proposes a vehicle localization system based on vehicle chassis sensors to improve the accuracy of vehicle stand-alone localization in highly dynamic driving conditions during GNSS outages. The system combines GNSS and on-board sensors data, and utilizes a fusion of vehicle dynamics and kinematics models, as well as an adaptive Kalman filter, to estimate the vehicle states. Experimental results demonstrate that the proposed method maintains high position accuracy during GNSS outages while the vehicle has lateral motion.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Peng Hang et al.
Summary: This research focuses on the collaborative control of four-wheel steering and direct yaw-moment control to enhance the active safety performance of automated electric vehicles. The study applies tube-based model predictive control to deal with disturbance and modeling error, and successfully designs an integrated controller that improves handling stability and path-tracking performance. Experimental tests prove the effectiveness and robustness of the integrated controller under extreme driving conditions.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Electrical & Electronic
Weiming Hu et al.
Summary: This paper proposes a rear anti-collision driving decision-making methodology based on deep reinforcement learning (RAD-DRL). By training and testing in a simulated environment, effective and reliable driving decisions are achieved, demonstrating its generalization and effectiveness in different driving scenarios.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Information Systems
Kan Zheng et al.
Summary: Autonomous driving vehicles can improve traffic conditions by reducing congestion, enhancing safety, and increasing traffic efficiency. This article focuses on a reinforcement-learning based method for intelligent behavior decision-making in autonomous vehicles. The proposed method optimizes driving quality by using a comprehensive reward function and leveraging knowledge of surrounding vehicles to predict behavior. Simulation results demonstrate a significant reduction in collision rate using this approach.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Automation & Control Systems
Wen Hu et al.
Summary: This study proposed a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks, utilizing utility theory and risk assessment for decision process, developing aggressiveness index and stability domains as constraints, providing human-like lane-change decisions and truck-friendly trajectories.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Engineering, Civil
Jinchao Hu et al.
Summary: The safety and efficiency of the merging zone are crucial for traffic networks. This paper proposes a roadside decision-making methodology to simultaneously improve safety and efficiency. Experimental results show that the method effectively increases the time to collision for commercial vehicles, reduces overall travel time, and decreases congestion length.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Seulbin Hwang et al.
Summary: This study proposes a reinforcement learning-based cut-in policy network nested within a finite state machine, aiming to achieve high cut-in performance without sacrificing safety. Simulation results show that this method significantly improves cut-in success rate and safety in high traffic scenarios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Asher Elmquist et al.
Summary: This article emphasizes the importance of simulation in the development and testing of robots and autonomous vehicles, focusing on the necessity of camera modeling for successfully simulating autonomous agents. It provides a detailed analysis of the image acquisition pipeline and algorithms used for modeling, as well as a discussion on data-driven approaches to camera modeling from a different perspective.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Civil
Nalin Kumar Sharma et al.
Summary: The study proposes an eco-driving technique for heavy-duty ego vehicles, which predicts the future speed of a leading vehicle based on its power capability observation, and optimizes the speed of the ego vehicle to minimize fuel consumption and maintain a safe distance. By using a leading vehicle observer, fuel savings of up to 8% are achieved compared to assuming constant speed of the leading vehicle.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Civil
Shuai Cheng et al.
Summary: This paper proposes a novel control scheme for automated steering systems based on human control behavior patterns, providing a universal solution for steering control in various driving scenarios. Simulation experiments demonstrate that the novel scheme outperforms traditional control schemes in steering performance, steering effort, and robustness.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Civil
Peng Hang et al.
Summary: This paper presents a human-like decision making framework for AVs considering the coexistence of human-driven vehicles and autonomous vehicles in the future. Different driving styles, social interaction characteristics, game theory, and model predictive control are applied for decision making in AVs. Testing scenarios of lane change show that game theoretic approaches can provide reasonable human-like decision making, with the Stackelberg game theory approach reducing the cost value by over 20% under normal driving style compared to the Nash equilibrium approach.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Mohammed Al-Qizwini et al.
Summary: A new simulation framework for learning control policies for autonomous vehicles is presented, based on real-world vehicle data and maps. The framework involves creating detailed lane-level maps, generating environments on these maps using vehicle sensor outputs, and learning control policies based on the constructed environment.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Yingji Xia et al.
Summary: This paper proposes a Human-like Lane Changing Intention Understanding Model (HLCIUM) for autonomous driving, which emulates the selective attention mechanism of human vision systems to understand the lane changing intentions of surrounding vehicles. The proposed model shows efficiency and robustness in complex real urban traffic datasets, making it ideal for use in human-like autonomous driving systems.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Transportation Science & Technology
Hongtao Yu et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2018)
Review
Transportation
S. M. Sohel Mahmud et al.
Article
Public, Environmental & Occupational Health
Xiaobo Qu et al.
TRAFFIC INJURY PREVENTION
(2014)
Article
Ergonomics
Qiang Meng et al.
ACCIDENT ANALYSIS AND PREVENTION
(2012)
Article
Engineering, Electrical & Electronic
Andrei Furda et al.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2011)
Article
Ergonomics
K Vogel
ACCIDENT ANALYSIS AND PREVENTION
(2003)
Article
Physics, Fluids & Plasmas
M Treiber et al.
