相关论文: Autonomous Vehicle front steering control computat…

Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks ranging from delivery to smart city surveillance. Reaping these benefits requires CAVs to…

This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a single front view camera. The employed method utilizes…

We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and steering commands given the objective of reaching a specific target…

Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety. Formation control is a typical multi-vehicle coordination method in the multi-lane scenario. Among the existing…

This paper proposes an Adaptive Robust Model Predictive Control strategy for lateral control in lane keeping problems, where we continuously learn an unknown, but constant steering angle offset present in the steering system. Longitudinal…

Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane…

Recent efforts in the development of autonomous driving technology have induced great advancements in perception, planning and control systems. Model predictive control is one of the most popular advanced control methods, but its…

This paper investigates the safe platoon formation tracking and merging control problem of connected and automated vehicles (CAVs) on curved multi-lane roads. The first novelty is the separation of the control designs into two distinct…

Under extreme conditions, autonomous drifting enables vehicles to follow predefined paths at large slip angles, significantly enhancing the control system's capability to handle hazardous scenarios. Four-wheel-drive and four-wheel-steering…

A convolutional neural network (CNN) approach is used to implement a level 2 autonomous vehicle by mapping pixels from the camera input to the steering commands. The network automatically learns the maximum variable features from the camera…

Predicting vehicle trajectories, angle and speed is important for safe and comfortable driving. We demonstrate the best predicted angle, speed, and best performance overall winning the top three places of the ICCV 2019 Learning to Drive…

We consider the problem of maximizing distance to road agents for a self-driving car. To this extent, we employ a Model Predictive Control (MPC) approach for the steering tracking control of an Autonomous Vehicle (AV). Specifically, we…

Adverse road conditions can cause vehicle yaw instability and loss of traction. To compensate for the instability under such conditions, corrective actions must be taken. In comparison to a mechanical differential, an electronic…

Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure the safety of systems with neural network based components,…

Driverless vehicles are complex systems operating in constantly changing environments. Automated driving is achieved by controlling the coupled longitudinal and lateral vehicle dynamics. Model predictive control is one of the most promising…

A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle.The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on.It…

In this study, we analyze the stability of a path-tracking controller designed for a four-wheel steering vehicle, incorporating the effects of the reference path curvature. By employing a simplified kinematic model of the vehicle with…

This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle. To this extent, two different artificial…

Lane change for autonomous vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guarantee safety as well as a high efficiency, AVs are inclined to choose relatively conservative…

Both humans and the sensors on an autonomous vehicle have limited sensing capabilities. When these limitations coincide with scenarios involving vulnerable road users, it becomes important to account for these limitations in the motion…