Advancements in Autonomous Systems and Transportation

The field of autonomous systems and transportation is rapidly evolving, with a focus on developing innovative solutions to improve efficiency, safety, and sustainability. Recent research has explored the integration of drones into truck delivery systems, optimizing logistics operations, and enhancing traffic signal control strategies. The use of reinforcement learning and deep learning techniques has been particularly noteworthy, enabling the development of more effective and adaptive control policies. Notable papers have proposed novel frameworks for autonomous navigation, traffic signal control, and electric autonomous mobility-on-demand systems, demonstrating significant improvements in performance and efficiency.

Some noteworthy papers include: Dynamic Switching Models for Truck-only Delivery and Drone-assisted Truck Delivery under Demand Uncertainty, which proposes a dynamic switching model to optimize logistics operations. GPLight+, which introduces a genetic programming method for learning symmetric traffic signal control policies. Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework, which presents a reinforcement learning framework for managing noise and safety in urban air mobility systems.

Sources

Dynamic Switching Models for Truck-only Delivery and Drone-assisted Truck Delivery under Demand Uncertainty

GPLight+: A Genetic Programming Method for Learning Symmetric Traffic Signal Control Policy

Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework

Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems

Autonomous UAV Flight Navigation in Confined Spaces: A Reinforcement Learning Approach

Optimizing Highway Traffic Flow in Mixed Autonomy: A Multiagent Truncated Rollout Approach

Combined Stochastic and Robust Optimization for Electric Autonomous Mobility-on-Demand with Nested Benders Decomposition

SWIRL: A Staged Workflow for Interleaved Reinforcement Learning in Mobile GUI Control

CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning

A Hierarchical Signal Coordination and Control System Using a Hybrid Model-based and Reinforcement Learning Approach

Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey

Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning

Single Agent Robust Deep Reinforcement Learning for Bus Fleet Control