Robust Autonomous Vehicle Pursuit without Expert Steering Labels

• URL: http://arxiv.org/abs/2308.08380v1
• Date: Wed, 16 Aug 2023 14:09:39 GMT
• Title: Robust Autonomous Vehicle Pursuit without Expert Steering Labels
• Authors: Jiaxin Pan, Changyao Zhou, Mariia Gladkova, Qadeer Khan and Daniel Cremers
• Abstract summary: We present a learning method for lateral and longitudinal motion control of an ego-vehicle for vehicle pursuit. The car being controlled does not have a pre-defined route, rather it reactively adapts to follow a target vehicle while maintaining a safety distance. We extensively validate our approach using the CARLA simulator on a wide range of terrains.
• Score: 41.168074206046164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present a learning method for lateral and longitudinal motion control of an ego-vehicle for vehicle pursuit. The car being controlled does not have a pre-defined route, rather it reactively adapts to follow a target vehicle while maintaining a safety distance. To train our model, we do not rely on steering labels recorded from an expert driver but effectively leverage a classical controller as an offline label generation tool. In addition, we account for the errors in the predicted control values, which can lead to a loss of tracking and catastrophic crashes of the controlled vehicle. To this end, we propose an effective data augmentation approach, which allows to train a network capable of handling different views of the target vehicle. During the pursuit, the target vehicle is firstly localized using a Convolutional Neural Network. The network takes a single RGB image along with cars' velocities and estimates the target vehicle's pose with respect to the ego-vehicle. This information is then fed to a Multi-Layer Perceptron, which regresses the control commands for the ego-vehicle, namely throttle and steering angle. We extensively validate our approach using the CARLA simulator on a wide range of terrains. Our method demonstrates real-time performance and robustness to different scenarios including unseen trajectories and high route completion. The project page containing code and multimedia can be publicly accessed here: https://changyaozhou.github.io/Autonomous-Vehicle-Pursuit/.

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