相关论文: Networked and Autonomous Model-scale Vehicles for …

We study learning based controllers as a replacement for model predictive controllers (MPC) for the control of autonomous vehicles. We concentrate for the experiments on the simple yet representative bicycle model. We compare training by…

For the optimum design of a driver-automation shared control system, an understanding of driver behavior based on measurements and modeling is crucial early in the development process. This paper presents a driver model through a weighting…

Autonomous driving has received a great deal of attention in the automotive industry and is often seen as the future of transportation. The development of autonomous driving technology has been greatly accelerated by the growth of…

Mobility scenarios involving short contact times pose a challenge for high bandwidth data transfer between autonomous vehicles and roadside base stations (BS). Millimeter wave bands are a viable solution as they offer enormous bandwidth in…

This paper presents a vehicle lateral controller based on spiking neural networks capable of replicating the behavior of a model-based controller but with the additional ability to perform online adaptation. By making use of neural…

Autonomous vehicles have the potential to revolutionize transportation, but they must be able to navigate safely in traffic before they can be deployed on public roads. The goal of this project is to train autonomous vehicles to make…

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which…

Model predictive control has emerged as an effective approach for real-time optimal control of connected and automated vehicles. However, nonlinear dynamics of vehicle and traffic systems make accurate modeling and real-time optimization…

We propose a new driving scheme, when different parts of a system are driven with different, generally incommensurate, frequencies. Such driving provides a flexible handle to control various properties of the system and to obtain new types…

AutoDRIVE is envisioned to be an integrated research and education platform for scaled autonomous vehicles and related applications. This work is a stepping-stone towards achieving the greater goal of realizing such a platform.…

Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular design tends to introduce a substantial amount of redundancy.…

Four-wheel independent steering (4WIS) systems provide mobile robots with a rich set of motion modes, such as Ackermann steering, lateral steering, and parallel movement, offering superior maneuverability in constrained environments.…

Emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, and electric vehicles, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better…

A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving…

The ability to reliably maximize tire force usage would improve the safety of autonomous vehicles, especially in challenging edge cases. However, vehicle control near the limits of handling has many challenges, including robustly contending…

The Driving World Model (DWM), which focuses on predicting scene evolution during the driving process, has emerged as a promising paradigm in the pursuit of autonomous driving (AD). DWMs enable AD systems to better perceive, understand, and…

Accurate modeling and control of autonomous vehicles remain a fundamental challenge due to the nonlinear and coupled nature of vehicle dynamics. While Koopman operator theory offers a framework for deploying powerful linear control…

The deployment of cooperative-intelligent transport systems (C-ITS) has started, and standardization and research activities are moving forward to improve road safety and vehicular efficiency. An aspect that is still felt as a limitation by…

One of the emerging applications that belong to ambient systems is to transparently and directly interconnect vehicles on roads, making an ad hoc network that enables a variety of applications through distributed software without the need…

Traffic Steering is a crucial technology for wireless networks, and multiple efforts have been put into developing efficient Machine Learning (ML)-enabled traffic steering schemes for Open Radio Access Networks (O-RAN). Given the swift…