Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach

Autonomous vehicle manufacturers recognize that LiDAR provides accurate 3D views and precise distance measures under highly uncertain driving conditions. Its practical implementation, however, remains costly. This paper investigates the optimal LiDAR configuration problem to achieve utility maximization. We use the perception area and non-detectable subspace to construct the design procedure as solving a min-max optimization problem and propose a bio-inspired measure -- volume to surface area ratio (VSR) -- as an easy-to-evaluate cost function representing the notion of the size of the non-detectable subspaces of a given configuration. We then adopt a cuboid-based approach to show that the proposed VSR-based measure is a well-suited proxy for object detection rate. It is found that the Artificial Bee Colony evolutionary algorithm yields a tractable cost function computation. Our experiments highlight the effectiveness of our proposed VSR measure in terms of cost-effectiveness configuration as well as providing insightful analyses that can improve the design of AV systems.Comment: 7 pages including the references, accepted by International Conference on Robotics and Automation (ICRA), 201

On Rendering Synthetic Images for Training an Object Detector

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available

Impact hazard protection efficiency by a small kinetic impactor

In this paper the ability of a small kinetic impactor spacecraft to mitigate an Earth-threatening asteroid is assessed by means of a novel measure of efficiency. This measure estimates the probability of a space system to deflect a single randomly-generated Earth-impacting object to a safe distance from the Earth. This represents a measure of efficiency that is not biased by the orbital parameters of a test-case object. A vast number of virtual Earth-impacting scenarios are investigated by homogenously distributing in orbital space a grid of 17,518 Earth impacting trajectories. The relative frequency of each trajectory is estimated by means Opik’s theory and Bottke’s near Earth objects model. A design of the entire mitigation mission is performed and the largest deflected asteroid computed for each impacting trajectory. The minimum detectable asteroid can also be estimated by an asteroid survey model. The results show that current technology would likely suffice against discovered airburst and local damage threats, whereas larger space systems would be necessary to reliably tackle impact hazard from larger threats. For example, it is shown that only 1,000 kg kinetic impactor would suffice to mitigate the impact threat of 27.1% of objects posing similar threat than that posed by Apophis