[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Wu et al., 2022 - Google Patents

Humanlike decision and motion planning for expressway lane changing based on artificial potential field

Wu et al., 2022

View PDF
Document ID
7381130425671894858
Author
Wu P
Gao F
Li K
Publication year
Publication venue
IEEE Access

External Links

Snippet

The autonomous vehicles (AVs) need to share the driving environment with the human driving vehicles (HDVs) on expressway in the future. The non-humanlike lane changing (LC) behavior of AVs can mislead human drivers, which brings potential risks. Stronger …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation

Similar Documents

Publication Publication Date Title
Wu et al. Humanlike decision and motion planning for expressway lane changing based on artificial potential field
Huang et al. Personalized trajectory planning and control of lane-change maneuvers for autonomous driving
Li et al. Combined trajectory planning and tracking for autonomous vehicle considering driving styles
Zhang et al. Adaptive decision-making for automated vehicles under roundabout scenarios using optimization embedded reinforcement learning
Gidado et al. A survey on deep learning for steering angle prediction in autonomous vehicles
Peng et al. An integrated model for autonomous speed and lane change decision-making based on deep reinforcement learning
Li et al. Combining decision making and trajectory planning for lane changing using deep reinforcement learning
Liu et al. Potential game-based decision-making for autonomous driving
Pan et al. A review of the development trend of adaptive cruise control for ecological driving
Tang et al. ATAC-based car-following model for level 3 autonomous driving considering driver’s acceptance
Xu et al. An integrated decision-making framework for highway autonomous driving using combined learning and rule-based algorithm
Shi et al. Physics-informed deep reinforcement learning-based integrated two-dimensional car-following control strategy for connected automated vehicles
Yang et al. Automated highway driving decision considering driver characteristics
Wang et al. Decision-making driven by driver intelligence and environment reasoning for high-level autonomous vehicles: a survey
Lu et al. Autonomous overtaking for intelligent vehicles considering social preference based on hierarchical reinforcement learning
Yao et al. Optimal lane-changing trajectory planning for autonomous vehicles considering energy consumption
Lu et al. Modeling of driver cut-in behavior towards a platoon
Yuan et al. Evolutionary decision-making and planning for autonomous driving based on safe and rational exploration and exploitation
Liu et al. Estimation of driver lane change intention based on the LSTM and Dempster–Shafer evidence theory
Yan et al. A hierarchical motion planning system for driving in changing environments: Framework, algorithms, and verifications
Azam et al. Driving behaviour modelling in the context of heterogeneous traffic and poor lane discipline conditions: the state of the art and beyond
Chen et al. Lane-exchanging driving strategy for autonomous vehicle via trajectory prediction and model predictive control
Yuan et al. Evolutionary Decision-Making and Planning for Autonomous Driving: A Hybrid Augmented Intelligence Framework
Wu et al. Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Wu et al. An integrated decision and motion planning framework for automated driving on highway