Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed

2024-12-20 · Benny Bao-Sheng Li, Elena Wu, Hins Shao-Xuan Yang, Nicky Yao-Jin Liang

Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (RL), we propose a novel approach to optimize driving policies that enable the vehicle to achieve near-maximum speed without compromising on safety or route accuracy, even in low-speed scenarios.

📄 PDF Abstract BibTeX arXiv:2412.16248

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous Driving reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Attention 설명 없음

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees

2024-07-23 · Tianyu Shi, Ilia Smirnov, Omar ElSamadisy, Baher Abdulhai

Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement Learning (RL) shows great promise for training autonomous driving controllers, being able to directly optimize a combi…

Autonomous Driving Reinforcement Learning (RL)

Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout

2023-10-16 · Jacob Paugh, Zhaoxuan Zhu, Shobhit Gupta, Marcello Canova 외

Connected and autonomous vehicles have the potential to minimize energy consumption by optimizing the vehicle velocity and powertrain dynamics with Vehicle-to-Everything info en route. Existing deterministic and stochast…

Autonomous Vehicles Stochastic Optimization

Multi-Objective Reinforcement Learning for Adaptive Personalized Autonomous Driving

2025-05-08 · Hendrik Surmann, Jorge de Heuvel, Maren Bennewitz

Human drivers exhibit individual preferences regarding driving style. Adapting autonomous vehicles to these preferences is essential for user trust and satisfaction. However, existing end-to-end driving approaches often …

Autonomous Driving Autonomous Vehicles Collision Avoidance Multi-Objective Reinforcement Learning+2

RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles

2025-02-27 · Ahmet Onur Akman, Anastasia Psarou, Łukasz Gorczyca, Zoltán György Varga 외

RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation, facilitating the testing and development of efficient route choice strategies for autonomous v…

Autonomous Vehicles Multi-agent Reinforcement Learning

DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving

2025-06-01 · Dawood Wasif, Terrence J Moore, Chandan K Reddy, Jin-Hee Cho

End-to-end autonomous driving systems map sensor data directly to control commands, but remain opaque, lack interpretability, and offer no formal safety guarantees. While recent vision-language-guided reinforcement learn…

Autonomous Driving Decoder Reinforcement Learning (RL)