A review of vision-based road detection technology for unmanned vehicles

With the development of unmanned vehicle technology, unmanned vehicles have played a huge role in logistics transportation, emergency rescue and disaster relief, etc., so the research on unmanned vehicles is becoming more and more important. Road detection is an important part of environmental perception and an important factor in the...

Formation Control of Skid-Steered Vehicles Based on Distributed Model Predictive Control

The skid-steered vehicle has the advantages of simple structure and strong maneuverability. Its formation driving can effectively improve safety, reduce energy consumption and exert its benefits, and has wide application prospects in military and civilian fields. Differential skid steering has strong horizontal and vertical coupling...

LQR Optimal Control of Four-steering Vehicle Based on Particle Swarm Optimization Algorithm

This paper proposes a linear quadratic controller based on particle swarm algorithm for the rear wheel control of four-wheel steering vehicle. Particle swarm optimization with fitness functions is used to optimize the coefficients of the weight matrix offline. The fuzzy rules following the controller is used if the road condition is terrible....

Occlusion Handling and Multi-scale Pedestrian Detection Based on Deep Learning: A Review

Pedestrian detection is an important branch of computer vision, and it has important applications in the fields of autonomous driving, artificial intelligence and video surveillance.With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and achieves better performance...

Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving

In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the...

Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning

As one of the main elements of reinforcement learning, the design of the reward function is often not given enough attention when reinforcement learning is used in concrete applications, which leads to unsatisfactory performances. In this study, a reward function matrix is proposed for training various decision-making modes with emphasis on...

Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Interactive Traffic Scenarios

A reliable multi-agent decision-making system is highly demanded for safe and efficient operations of connected and autonomous vehicles (CAVs). In order to represent the mutual effects between vehicles and model the dynamic traffic environments, this research proposes an integrated and open-source framework to realize different Graph...