The objective of this paper is to detail the design and development of a unique VIL dynamometer system for testing lateral and longitudinal control using simulated and/or recorded data. A floating hub design decouples the vehicle hub rotation while continuing to support the vehicle mass on the tires and suspension of the vehicle. Long travel CV-joints are used to enable full angular displacement/turning radius of the steering wheels and couple the drive axles to the absorbing dynamometer motors. The steering wheels are placed on three degree of freedom (3DOF) motion plates allowing for rotation and translation during steering articulation. The reduced rotary mass and inertia of non-rotating tires however influences the tire-forces when performing lateral maneuvers. A common approach to this problem has been to attach a resisting actuator, like a chain drive, to the rotary base where the wheel is resting. Because additional degrees of freedom are introduced by the use of 3DOF motion plates, this approach is not suitable. To compensate for the lack of physical accuracy without introducing additional hardware to interfere with the wheel or rotary base, the modification of the performance curves directly associated with the electric power steering system is explored. Using experimentally obtained wheel forces, the power steering performance curve can be modified to a function of vehicle velocity. The vehicle’s current velocity can be obtained via CAN from its On-Board-Diagnosis (OBD) system. Improving the physical accuracy of hub-mounted dynamometer systems during lateral maneuvers introduces a cost and space-efficient research platform for the development and testing of automated driving systems and automotive components.
Vehicle platooning allows a follower vehicle to reduce energy consumption by following closely behind a cooperative lead vehicle. In a previous work, we described a lidar-based vehicle detection and tracking approach that measures the extents of the back surface of the lead vehicle and allows for automated lane level positioning. In this work, we introduce a follower vehicle energy model and compare the performance of a vehicle centering strategy based on rear backplane geometry as opposed to center of mass and bounding box approaches. Energy efficiency improvements are discussed with respect to the computational complexity of each approach.
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.
We propose a modification to the popular pure pursuit algorithm for path following of car like platforms in which multiple look-ahead points along the target path are aggregated to form a spatially filtered steering command. Our approach enables complex following behaviors while avoiding such as oscillatory behavior observed in approaches with a single look-ahead point.
In a human operated vehicle, the alignment of tires aims to strike a balance between ease of steering and a minimization of tire wear. The replacement of the human driver in an autonomous vehicle with low latency computer control of path tracking means that tire alignment can be performed with less emphasis on handling characteristics which contribute to ease of steering and directed towards improvement in tire life. This study uses MATLABs Vehicle Dynamics Blockset and Predictive Driver block to compare the path tracking capability of a passenger vehicle performing a double lane change maneuver under the control of the pure pursuit autonomous path following algorithm as well as a simulated human driver. Validation of the Predictive Driver block is performed by tracking a panel of human drivers performing the double lane change maneuver using GPS for localization in a subcompact electric vehicle. The vehicle model is characterized based on measurements from the test vehicle and sent through the same double lane change in simulation to compare behaviors. Tire alignment parameters are altered to demonstrate their effects on vehicle handling under both types of vehicle control. In the simulation environment, the pure pursuit algorithm tracks the desired path consistently across all parameter variations while the simulated human driver varies in its path tracking capabilities.
For autonomous systems and vehicles to be able to traverse and path plan in their environment, location needs to be known. To determine location, two items are required, a map and a localization method. The purpose of this paper is to determine the differences and benefits between two low cost off the shelf Light Detection And Ranging (LiDAR) units in an unstructured environment for mapping and localization. The LiDAR units angularly sample the environment in a full 360-degree horizontal view, each with a varying sample density. The first has more vertical samples and a broader vertical field of view, where the other has fewer vertical samples and smaller vertical field of view but higher angular resolution. Also, both LiDAR units produce similar orders of magnitude of points per second. The two LiDAR units are compared with a simulation model of the test vehicle and LiDAR units as well as real world testing. The results of the experiments show that that mapping with a higher vertical resolution LiDAR unit appears to produce a better map, whereas localization with the higher angular resolution LiDAR unit produces more consistent results overall but runs considerably slower.
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