Steer with Me: A Predictive, Potential Field-Based Control Approach for Semi-Autonomous, Teleoperated Road Vehicles

Schimpe, Andreas; Diermeyer, Frank

doi:10.1109/itsc45102.2020.9294702

Cited by 21 publications

(16 citation statements)

References 19 publications

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“…In Direct Control mode, the primary control commands are directly transmitted to the vehicle for execution, i.e., the operator controls the vehicle at stabilization level. Other control modes, such as a shared control approach [23] or a concept to modify the perception of the vehicle [24] are also under development and will be integrated into the software stack.…”

Section: E Controlmentioning

confidence: 99%

Open Source Software for Teleoperated Driving

Schimpe¹,

Feiler²,

Hoffmann³

et al. 2021

Preprint

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Teleoperation allows a human operator to remotely interact with and control a mobile robot in a dangerous or inaccessible area. Besides well-known applications such as space exploration or search and rescue operations, the application of teleoperation in the area of automated driving, i.e., teleoperated driving (ToD), is becoming more popular. Instead of an in-vehicle human fallback driver, a remote operator can connect to the vehicle using cellular networks and resolve situations that are beyond the automated vehicle (AV)'s operational design domain. Teleoperation of AVs, and unmanned ground vehicles in general, introduces different problems, which are the focus of ongoing research. This paper presents an open source ToD software stack, which was developed for the purpose of carrying out this research. As shown in three demonstrations, the software stack can be deployed with minor overheads to control various vehicle systems remotely.

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Section: E Controlmentioning

confidence: 99%

Open Source Software for Teleoperated Driving

Schimpe¹,

Feiler²,

Hoffmann³

et al. 2021

Preprint

Self Cite

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“…To address the above challenges, this paper presents an active safety system for collision avoidance using Model Predictive Control (MPC) in a shared control framework specifically for a teleoperated road vehicle, building on the approach presented in [4]. First, we propose a driver assistance system (Sec.…”

Section: A Contributionsmentioning

confidence: 99%

“…The primary objective is to track the input references as generated by the human operator and intervene when deemed necessary to avoid collision with static or dynamic obstacles. As compared to [4], the approach presented in this paper controls both, the longitudinal and lateral dynamics of the vehicle. In addition, our design introduces a restriction on the controller's authority to override human operator to improve the operator trust on the automation.…”

Section: A Contributionsmentioning

confidence: 99%

“…Tuning of this threat metric is not trivial and only lateral control was showcased in this work. Finally, a model-based uncoupled shared control approach is followed in [9], [10] and [4]. In [9], a model predictive contouring control (MPCC) framework is used for automation where center of the lane is considered as a reference path.…”

Section: B Related Workmentioning

confidence: 99%

“…It becomes difficult to judge velocity and location of the obstacles and this problem aggravates in the peripheral region of the view. In tight driving scenarios, the chances of collision increase and hence a driver assistance system to help avoid collision proves beneficial as shown in [4]. As Fig.…”

Section: Introductionmentioning

confidence: 99%

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Active Safety System for Semi-Autonomous Teleoperated Vehicles

Saparia¹,

Schimpe²,

Ferranti³

2021

Preprint

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Autonomous cars can reduce road traffic accidents and provide a safer mode of transport. However, key technical challenges, such as safe navigation in complex urban environments, need to be addressed before deploying these vehicles on the market. Teleoperation can help smooth the transition from human operated to fully autonomous vehicles since it still has human in the loop providing the scope of fallback on driver. This paper presents an Active Safety System (ASS) approach for teleoperated driving. The proposed approach helps the operator ensure the safety of the vehicle in complex environments, that is, avoid collisions with static or dynamic obstacles. Our ASS relies on a model predictive control (MPC) formulation to control both the lateral and longitudinal dynamics of the vehicle. By exploiting the ability of the MPC framework to deal with constraints, our ASS restricts the controller's authority to intervene for lateral correction of the human operator's commands, avoiding counter-intuitive driving experience for the human operator. Further, we design a visual feedback to enhance the operator's trust over the ASS. In addition, we propose an MPC's prediction horizon data based novel predictive display to mitigate the effects of large latency in the teleoperation system. We tested the performance of the proposed approach on a high-fidelity vehicle simulator in the presence of dynamic obstacles and latency.

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