Conception of control paradigms for teleoperated driving tasks in urban environments

Development of concepts and computationally efficient motion planning methods for teleoperated drivingEntwicklung von Konzepten und recheneffizienten Bewegungsplanungsmethoden für teleoperiertes Fahre

A Survey on Remote Operation of Road Vehicles

In recent years, the use of remote operation has been proposed as a bridge towards driverless mobility by providing human assistance remotely when an automated driving system finds a situation that is ambiguous and requires input from a remote operator. The remote operation of road vehicles has also been proposed as a way to enable drivers to operate vehicles from safer and more comfortable locations. While commercial solutions for remote operation exist, remaining challenges are being tackled by the research community, who is continuously testing and validating the feasibility of deploying remote operation of road vehicles on public roads. These tests range from the technological scope to social aspects such as acceptability and usability that affect human performance. This survey presents a compilation of works that approach the remote operation of road vehicles. We start by describing the basic architecture of remote operation systems and classify their modes of operation depending on the level of human intervention. We use this classification to organize and present recent and relevant work on the field from industry and academia. Finally, we identify the challenges in the deployment of remote operation systems in the technological, regulatory, and commercial scopes

Progress toward multi‐robot reconnaissance and the MAGIC 2010 competition

Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human‐robot interfaces. This paper describes our 14‐robot team, which won the MAGIC 2010 competition. It was designed to perform urban reconnaissance missions. In the paper, we describe a variety of autonomous systems that require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, which is essential for autonomous planning and for giving humans situational awareness, required the development of fast loop‐closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. We will describe technical contributions throughout our system that played a significant role in its performance. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93532/1/21426_ftp.pd

Design and implementation of a teleoperator’s workstation

Treball desenvolupat en el marc del programa "European Project Semester".The project aims to implement a way for a teleoperator to control an existing self-driving car if the autonomous driving algorithms fail to respond to the encountered situation. The project will rely on the existing code developed by the MechLab Team at the HTW in Dresden, who have converted a BMW i3 into a self-driving car using surround and proximity sensors and a homemade software that controls the vehicle's speed and steering. The car is also able to detect pedestrians and other obstacles thanks to a deep learning algorithm dedicated to this part. Teleoperation systems pose many challenges, such as providing the teleoperator with the same level of situational awareness as a driver in the car. The driver needs to focus more on the surroundings, and therefore teleoperated drivers will have to rest more often and take more breaks. To address this challenge, the teleoperation system will use high information density sensors, including LiDAR, radar, and ultrasonic sensors, to provide the driver with an overlay of detected obstacles and the predicted path, enhancing reality to compensate for latency in communication by taking some workload off the operator. Another big challenge is to switch between the autonomous and teleoperated driving modes, as there are different problems that can appear. Most noticeably, during the time it takes for the operator to get aware of the situation and respond to the call, the car must be able to safely stop and wait for instructions from the operator. The failure to do so could result in dangerous or even deadly situations for the autonomous vehicle’s occupants as well as for the other road users, who do not need to wait for the communication to be established. One of the last great challenges is allow stable and fast communication between the car and the teleoperator. This can be achieved by narrowing the data transmitted for example by reducing video quality in predefined cases, or by ensuring redundancy in the communication media. Nevertheless, a complete loss of communication is not impossible, so a protocol needs to be defined in order to safely halt the vehicle while waiting on the reconnection of the transmission. To fulfil this project, our team will use MATLAB and Simulink in combination with different toolboxes from the MathWorks company. We will try to develop a human-machine interface for the teleoperator, implement a way for the operator to take over control of the vehicle, build scenarios to test and simulate our different programs and much more. All of this is done in order to build safer and more reliable autonomous vehicles for the future.Incomin

2021 Vehicle Dynamics seminar

The seminar is held annually. The full title of this year\u27s seminar was "2021 Vehicle Dynamics seminar -- for Future Mobility ...and not only Lateral"

Design, Implementation, and Empirical Validation of a Framework for Remote Car Driving Using a Commercial Mobile Network

Despite the fact that autonomous driving systems are progressing in terms of their automation levels, the achievement of fully self-driving cars is still far from realization. Currently, most new cars accord with the Society of Automotive Engineers (SAE) Level 2 of automation, which requires the driver to be able to take control of the car when needed: for this reason, it is believed that between now and the achievement of fully automated self-driving car systems, there will be a transition, in which remote driving cars will be a reality. In addition, there are tele-operation-use cases that require remote driving for health or safety reasons. However, there is a lack of detailed design and implementation available in the public domain for remote driving cars: therefore, in this work we propose a functional framework for remote driving vehicles. We implemented a prototype, using a commercial car. The prototype was connected to a commercial 4G/5G mobile network, and empirical experiments were conducted, to validate the prototype’s functions, and to evaluate its performance in real-world driving conditions. The design, implementation, and empirical evaluation provided detailed technical insights into this important research and innovation area.This research was funded in part by the EU Horizon 2020 5G-PPP 5G-INDUCE project (“Open cooperative 5G experimentation platforms for the industrial sector NetApps”) under grant number H2020-ICT-2020-2/101016941, by the EU Horizon Europe INCODE project (“Programming platform for intelligent collaborative deployments over heterogeneous edge-IoT environments”) under grant number HORIZON-CL4-2022-DATA-01-03/101093069, and by the EU Horizon Europe project INCODE: programming platform for intelligent collaborative deployments over heterogeneous edge-IoT environments (HORIZON-CL4-2022-DATA-01-03/101093069)