The Perception Modification Concept to Free the Path of An Automated Vehicle Remotely

Feiler, Johannes; Diermeyer, Frank

doi:10.5220/0010433304050412

Cited by 4 publications

(3 citation statements)

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“…Interactive path planning [27], [28] uses the vehicle's perception module to calculate optimal paths, which the operator confirms to follow, bypassing network latency but requiring a functional set of AD perception module. Perception modification [29] involves the operator identifying false-positive obstacles to support the AD perception module, which largely depends on the availability of AD perception module.…”

Section: Performance Of Successive Reference Pose Tracking Vs Smith P...mentioning

confidence: 99%

Performance of Successive Reference Pose Tracking vs Smith Predictor Approach for Direct Vehicle Teleoperation Under Variable Network Delays

Prakash,

Vignati,

Sabbioni

2024

IEEE Trans. Veh. Technol.

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Section: Performance Of Successive Reference Pose Tracking Vs Smith P...mentioning

confidence: 99%

Performance of Successive Reference Pose Tracking vs Smith Predictor Approach for Direct Vehicle Teleoperation Under Variable Network Delays

Prakash,

Vignati,

Sabbioni

2024

IEEE Trans. Veh. Technol.

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“…[28], [29] Perception modification: The human operator supports the AD perception module by identifying false-positive obstacles. It majorly depends upon the AD modules [30].…”

Section: Introductionmentioning

confidence: 99%

“…Interactive path planning [25], [26] uses the vehicle's perception module to calculate optimal paths, which the operator confirms to follow, bypassing network latency but requiring a functional set of AD perception module. Perception modification [27] involves the operator identifying false-positive obstacles to support the AD perception module, which largely depends on the availability of AD perception module.…”

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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ODD Taxi

Diermeyer,

Raudszus,

Wimmershoff

2024

Handbuch Assistiertes Und Automatisiertes Fahren

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The Perception Modification Concept to Free the Path of An Automated Vehicle Remotely

Cited by 4 publications

References 0 publications

Performance of Successive Reference Pose Tracking vs Smith Predictor Approach for Direct Vehicle Teleoperation Under Variable Network Delays

Performance of Successive Reference Pose Tracking vs Smith Predictor Approach for Direct Vehicle Teleoperation Under Variable Network Delays

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

ODD Taxi

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