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Intelligent Intersection Navigation ...

Sassi, Ayoub,

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Intelligent Intersection Navigation Enhancing Autonomous Driving With Reinforcement Learning and Bird's Eye View Fusion /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent Intersection Navigation Enhancing Autonomous Driving With Reinforcement Learning and Bird's Eye View Fusion // Ayoub Sassi.
Author:	Sassi, Ayoub,
Description:	1 electronic resource (62 pages)
Notes:	Source: Masters Abstracts International, Volume: 87-11.
Contained By:	Masters Abstracts International87-11.
Subject:	Automotive engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32668563
ISBN:	9798241691668

Intelligent Intersection Navigation Enhancing Autonomous Driving With Reinforcement Learning and Bird's Eye View Fusion /
Sassi, Ayoub,

Intelligent Intersection Navigation Enhancing Autonomous Driving With Reinforcement Learning and Bird's Eye View Fusion /Ayoub Sassi. - 1 electronic resource (62 pages)

Source: Masters Abstracts International, Volume: 87-11.

In recent years, the automotive sector has witnessed significant advancements driven by the integration of Artificial Intelligence (AI). Autonomous driving systems are reshaping modern transportation by improving safety, efficiency, and accessibility. However, handling complex traffic situations remains a major challenge due to their dynamic and uncertain nature. Conventional rule-based approaches often lack the flexibility required to manage such environments, highlighting the need for more adaptive and intelligent solutions. In this work, we address the problem of autonomous navigation at intersections, one of the most challenging scenarios in urban driving. We introduce a novel approach that combines Bird's Eye View (BEV) representation with Reinforcement Learning (RL) to enhance decision-making capabilities. Using the CARLA simulator as a controlled environment for training and evaluation, we first fine-tune the UNetXST model to generate a fused BEV representation from multiple camera views. This unified perspective provides a comprehensive understanding of the surrounding environment and serves as input to the learning agent. Then, we employ the Proximal Policy Optimization (PPO) algorithm to train an RL agent capable of learning effective driving strategies through continuous interaction with the simulated environment. The objective is to achieve safe and efficient intersection traversal while minimizing collisions and ensuring smooth traffic behavior. This work contributes toward the development of more robust autonomous driving systems, with potential implications for enhancing road safety and optimizing urban mobility.

English

ISBN: 9798241691668Subjects--Topical Terms:

2181195
Automotive engineering.
Subjects--Index Terms:

Autonomous driving systems

Intelligent Intersection Navigation Enhancing Autonomous Driving With Reinforcement Learning and Bird's Eye View Fusion /
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520 $a In recent years, the automotive sector has witnessed significant advancements driven by the integration of Artificial Intelligence (AI). Autonomous driving systems are reshaping modern transportation by improving safety, efficiency, and accessibility. However, handling complex traffic situations remains a major challenge due to their dynamic and uncertain nature. Conventional rule-based approaches often lack the flexibility required to manage such environments, highlighting the need for more adaptive and intelligent solutions. In this work, we address the problem of autonomous navigation at intersections, one of the most challenging scenarios in urban driving. We introduce a novel approach that combines Bird's Eye View (BEV) representation with Reinforcement Learning (RL) to enhance decision-making capabilities. Using the CARLA simulator as a controlled environment for training and evaluation, we first fine-tune the UNetXST model to generate a fused BEV representation from multiple camera views. This unified perspective provides a comprehensive understanding of the surrounding environment and serves as input to the learning agent. Then, we employ the Proximal Policy Optimization (PPO) algorithm to train an RL agent capable of learning effective driving strategies through continuous interaction with the simulated environment. The objective is to achieve safe and efficient intersection traversal while minimizing collisions and ensuring smooth traffic behavior. This work contributes toward the development of more robust autonomous driving systems, with potential implications for enhancing road safety and optimizing urban mobility.
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650 4 $a Automotive engineering. $3 2181195
650 4 $a Computer science. $3 523869
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653 $a Proximal Policy Optimization
653 $a Reinforcement Learning
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690 $a 0540
690 $a 0984
710 2 $a University of Michigan-Flint. $b Computer Science & Information Systems. $e degree granting institution. $3 3814668
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773 0 $t Masters Abstracts International $g 87-11.
790 $a 1873
791 $a M.S.
792 $a 2026
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