Robust Autonomous Vehicle Pursuit without Expert Steering Labels
Jiaxin Pan Changyao Zhou Mariia Gladkova Qadeer Khan Daniel Cremers
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/.