Exploiting Multi-Modal Fusion for Urban Autonomous Driving Using Latent Deep Reinforcement Learning

Author:

Khalil Yasser H.1ORCID,Mouftah Hussein T.1ORCID

Affiliation:

1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

Funder

Canada Research Chairs Fund Program

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Multi-Teacher Policy Distillation Framework for Enhancing Zero-Shot Generalization of Autonomous Driving Policies;IEEE Transactions on Vehicular Technology;2024-07

2. Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey;Transportation Research Part C: Emerging Technologies;2024-07

3. Research on reinforcement learning based on PPO algorithm for human-machine intervention in autonomous driving;Electronic Research Archive;2024

4. Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning;2023 11th RSI International Conference on Robotics and Mechatronics (ICRoM);2023-12-19

5. Design of an Efficient Mathematical Optimization Engine for Solving Autonomous Driving Performance for Urban Traffic Conditions;2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON);2023-12-01

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