Distant Vehicle Detection Using Radar and Vision

Abstract – For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using convolutional neural networks exhibit excellent performance on existing datasets such as KITTI. However, the performance of these networks falls when detecting small (distant) objects. We demonstrate that incorporating radar data can boost performance in these difficult situations. We also introduce an efficient automated method for training data generation using cameras of different focal lengths.

Further Info – For more information please read our paper:

  • [PDF] S. Chadwick, W. Maddern, and P. Newman, “Distant Vehicle Detection Using Radar and Vision,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, 2019.
    [Bibtex]
    @InProceedings{ICRA19_chadwick,
    author = {Chadwick, Simon and Maddern, Will and Newman, Paul},
    title = {Distant Vehicle Detection Using Radar and Vision},
    year = {2019},
    address = {Montreal, Canada},
    pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/ICRA19_chadwick.pdf},
    booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
    }