Abstract
This is motivated by a requirement for robust, autonomy-enabling scene understanding in unknown environments. In the method proposed in this paper, discriminative machine-learning approaches are applied to infer traversability and predict routes from Frequency-Modulated Contunuous-Wave (FMCV) radar frames. Firstly, using geometric features extracted from LiDAR point clouds as inputs to a fuzzy-logic rule set, traversability pseudo-labels are assigned to radar frames from which weak supervision is applied to learn traversability from radar. Secondly, routes through the scanned environment can be predicted after they are learned from the odometry traces arising from traversals demonstrated by the autonomous vehicle (AV). In conjunction, therefore, a model pretrained for traversability prediction is used to enhance the performance of the route proposal architecture. Experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community. Our key finding is that joint learning of traversability and demonstrated routes lends itself best to a model which understands where the vehicle should feasibly drive. We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network. We conclude that the proposed system will find use in enabling mapless vehicle autonomy in extreme environments.
Highlights
As we move towards higher levels of vehicle autonomy, the need for sensors robust to a diverse range of environmental conditions has driven increased interest in radar
We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network
An appearance-based radar traversability methodology was introduced and shown to infer traversability maps that are robust to occlusion and artefacts while exceeding the range of existing vision and LiDAR methodologies
Summary
As we move towards higher levels of vehicle autonomy, the need for sensors robust to a diverse range of environmental conditions has driven increased interest in radar. Autonomy—to reduce the dependency of autonomous vehicles on high resolution maps—it is necessary for vehicles to have an understanding of traversability to facilitate robust path planning in novel environments Both LiDARs and cameras operate within a narrow frequency band in the electromagnetic spectrum (905 nm to 1550 nm for LiDAR sensors and 400 nm to 700 nm for cameras). Frequency-Modulated Continuous-Wave (FMCW) radar operates in the range of 2 mm to 12.5 mm [1], allowing for increased material penetration and negligible solar interference. This allows for negligible attenuation in the presence of rain or snow [2]. Deterministic models of traversability are difficult to formulate from radar data alone
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
