Active Safety System for Semi-Autonomous Teleoperated Vehicles

Saparia, Smit; Schimpe, Andreas; Ferranti, Laura

doi:10.1109/ivworkshops54471.2021.9669239

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…They also introduce a semi-autonomous controller "to correct the steering input given by the teleoperator if the vehicle is at risk of hitting an obstacle." Saparia et al [40] and Hoffmann and Diermeyer [27] also follow these terms.…”

Section: Research Approachesmentioning

confidence: 89%

Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems

Bogdoll,

Orf,

Töttel

et al. 2021

Preprint

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Corner cases for driving automation systems can often be detected by the system itself and subsequently resolved by remote humans. There exists a wide variety of approaches on how remote humans can resolve such issues. Over multiple domains, no common taxonomy on those approaches has developed yet. As the scaling of automated driving systems continues to increase, a uniform taxonomy is desirable to improve communication within the scientific community, but also beyond to policymakers and the general public. In this paper, we provide a survey on recent terminologies and propose a taxonomy for remote human input systems, classifying the different approaches based on their complexity.

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Section: Research Approachesmentioning

confidence: 89%

Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems

Bogdoll,

Orf,

Töttel

et al. 2021

Preprint

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“…In consequence, multiple approaches address this issue, given the capability of also overriding the human's longitudinal control command, i.e., desired velocity or acceleration. This has proven to effectively improve vehicle safety in environments with static [14] and dynamic obstacles [15,16]. Throughout the years, cruise control, which only decouples longitudinal control from the driver, has been designed for different tasks and purposes.…”

Section: Related Workmentioning

confidence: 99%

“…The baseline shared control approach is based on MPC. Closely following the formulation presented in [16], it is capable of velocity and steering intervention. Early in the prediction horizon, the MPC aims to track the current operator control command.…”

Section: A Simulation Setupmentioning

confidence: 99%

Steering Action-aware Adaptive Cruise Control for Teleoperated Driving

Schimpe¹,

Majstorovic²,

Diermeyer³

2022

Preprint

Self Cite

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In this paper, a steering action-aware Adaptive Cruise Control (ACC) approach for teleoperated road vehicles is proposed. In order to keep the vehicle in a safe state, the ACC approach can override the human operator's velocity control commands. The safe state is defined as a state from which the vehicle can be stopped safely, no matter which steering actions are applied by the operator. This is achieved by first sampling various potential future trajectories. In a second stage, assuming the trajectory with the highest risk, a safe and comfortable velocity profile is optimized. This yields a safe velocity control command for the vehicle. In simulations, the characteristics of the approach are compared to a Model Predictive Control-based approach that is capable of overriding both, the commanded steering angle as well as the velocity. Furthermore, in teleoperation experiments with a 1:10-scale vehicle testbed, it is demonstrated that the proposed ACC approach keeps the vehicle safe, even if the control commands from the operator would have resulted in a collision.

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“…Even though it makes no attempt to simulate the forces of ground contact and traction, it has shown to be more than adequate for replicating the network delays of less than a second. Smit Saparia et al [ 18 ] introduced an active safety system for collision avoidance in vehicle teleoperation. It receives the usual steering and reference velocity commands from the control station, but modulates them as per the potential fields of the obstacles.…”

Section: Introductionmentioning

confidence: 99%

“…Direct control [6]- [13] involves the operator viewing sensor data and sending control signals like steering and throttle, but it suffers from reduced situational awareness and transmission latency. Shared control [14]- [19] has a shared controller inside the vehicle that assesses operator commands to avoid collisions, improving safety but still suffering from latency. Trajectory guidance [20]- [24] involves the vehicle following a path and speed profile generated by the operator without being affected by network latency, although real-time profile generation is unfeasible.…”

mentioning

confidence: 99%

Vehicle Teleoperation: Human in the Loop Performance Comparison of Smith Predictor with Novel Successive Reference-Pose Tracking Approach

Prakash

Vignati

Sabbioni

et al. 2022

Sensors

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Vehicle teleoperation has the ability to bridge the gap between completely automated driving and manual driving by remotely monitoring and operating autonomous vehicles when their automation fails. Among many challenges related to vehicle teleoperation, the considered ones in this work are variable time delay, saturation of actuators installed in vehicle, and environmental disturbance, which together limit the teleoperation performance. State-of-the-art predictive techniques estimate vehicle states to compensate for the delays, but the predictive states do not account for sudden disturbances that the vehicle observes, which makes the human-picked steer inadequate. This inadequacy of steer deteriorates the path-tracking performance of vehicle teleoperation. In the proposed successive reference-pose-tracking (SRPT) approach, instead of transmitting steering commands, the reference trajectory, in the form of successive reference poses, is transmitted to the vehicle. This paper introduces a method of generation of successive reference poses with a joystick steering wheel and compares the human-in-loop path-tracking performance of the Smith predictor and SRPT approach. Human-in-loop experiments (with 18 different drivers) are conducted using a simulation environment that consists of the integration of a real-time 14-DOF Simulink vehicle model and Unity game engine in the presence of bidirectional variable delays. Scenarios for performance comparison are low adhesion ground, strong lateral wind, tight corners, and sudden obstacle avoidance. Result shows significant improvement in reference tracking and in reducing human effort in all scenarios using the SRPT approach.

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Active Safety System for Semi-Autonomous Teleoperated Vehicles

Cited by 10 publications

References 14 publications

Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems

Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems

Steering Action-aware Adaptive Cruise Control for Teleoperated Driving

Vehicle Teleoperation: Human in the Loop Performance Comparison of Smith Predictor with Novel Successive Reference-Pose Tracking Approach

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