The passing of a wheeled or tracked vehicle over soft or deformable soil creates ruts. The depth of these ruts is proportional to the weight of the vehicle and the soil trafficability; the ability of the soil to support traffic from vehicles. Assessing soil trafficability is often a manual and labor-intensive process. We evaluate the ability of lidar and depth cameras to detect changes in rut depth with the goal of minimizing manual or automated evaluation via soil strength testing. Our sensor-based approach mimics the process used by human operators when measuring rut depth. We compare this approach with machine-centered approaches with the goal of improving correlation between soil strength measurements and rut depth. In general, we find that all sensors are able to measure rut depth within the uncertainty bounds of soil and rut depth models for light vehicles.
In addition to providing convenience and improving safety autonomous vehicle technologies offer an opportunity to reduce energy use by up to twenty percent or more. One strategy for reducing energy use is careful positioning of an autonomous vehicle, the ego vehicle, behind one or more lead vehicles. Most perception pipelines fit a bounding box around the center of mass of a detected object. That approach may not be accurate enough to allow for precise positioning. Here we compare different methods of identifying vehicle boundaries and vehicle type using a combination of simulation and field testing. Approaches will be compared based on required LiDAR resolution and algorithm complexity relative to potential improvement in energy efficiency.
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