Automated vehicles

This Technical Summary pertains to report 2019-35, How Locals Need to Prepare for the Future of V2V/V2I Connected Vehicles.

This Technical Summary pertains to report 2019-18, Preparing Local Agencies for the Future of Connected and Autonomous Vehicles.

This Technical Summary pertains to the LRRB-produced Report 2016-02, “The Transportation Futures Project: Planning for Technology Change,” published January 2016.

This is a group of photos about Med City Mover. The Med City Mover is a Minnesota Department of Transportation-led research project that is testing and demonstrating two low-speed, automated, electric, multi-passenger shuttles in downtown Rochester, MN.

An interactive presentation describing frequently asked questions about Connected and Autonomous Vehicles (CAV). The presentation is divided into five sections: General, Timing/Preparation, Policy/Standardization, Safety, Rural, and Resources.

This Technical Summary pertains to report 2022-03, Can Automated Vehicles "See" in Minnesota? Ambient Particle Effects on LiDAR.

This project will use a combination of laboratory experimentation and road demonstrations to better understand the reduction of LiDAR signal and object detection capability under adverse weather conditions found in Minnesota. It will also lead to concepts to improve LiDAR systems to adapt to such conditions through better signal processing image recognition software.

Basic Safety Message (BSM) containing data about the vehicle's position, speed, and acceleration. Roadside receivers, RSUs, can capture BSM broadcasts and translate them into information about traffic conditions. If every vehicle is equipped with awareness, BSMs can be combined to calculate traffic flows, speeds, and densities. These three key parameters will be post-processed to obtain queue lengths and travel time estimates. The project team proposed a traffic state estimation algorithm using BSMs based on the Kalman filter technique. The algorithm's performance was tested with BSMs generated from several arterial in a microscopic simulation model and BSMs generated with radar data collected on freeway sections. Then the project team developed a traffic monitoring system to apply the algorithm to a large-scale network with different types of roads. In the system, computers could remotely access the online server to acquire BSMs and estimate traffic states in real-time.