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Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.

People through many years of experience, have developed a great intuitive sense for navigation and spatial awareness. With this intuition people are able to apply a near rules based approach to their driving. With a transition to autonomous driving, these intuitive skills need to be taught to the system which makes perception is the most fundamental and critical task. One of the major challenges for autonomous vehicles is accurately knowing the position of the vehicle relative to the world frame. Currently, this is achieved by utilizing expensive sensors such as a differential GPS which provides centimeter accuracy, or by using computationally taxing algorithms to attempt to match live input data from LiDARs or cameras to previously recorded data or maps. Within this paper an algorithm and accompanying hardware stack is proposed to reduce the computational load on the localization of the robot relative to a prior map.

A total water content probe for flight- and ground-based testing is being completed. During operation across a range of altitudes and water content conditions, the probe has to maintain isokinetic flow, vaporize the solid and liquid water content and maintain the inlet ice free to ensure isokinetic flow. Despite achieving isokinetic operation, the collection efficiency of particles less than 30 μm can be less than 100%. A correlation of collection efficiency to Stokes number has been determined to correct the results for this effect. In preparation for flight testing an integrated data acquisition, control and power supply unit was developed and successfully tested. Results from testing at the NASA Glenn Icing Research Tunnel are presented covering both ice crystals and super-cooled liquid conditions. The results correspond well to previously published work and problems encountered during previous testing of this probe are shown to have been resolved.

Controlling the forming of large thermoplastic parts from a simulation requires very precise predictions of the pressure and volume profile evolution. Present pressure profile based simulations adequately predict the thickness distribution of a part, but the forming pressure and volume profile development lack the precision required for process control. However new simulations based on the amount of power required to form the material can accurately predict these pressure and volume profiles. In addition online monitoring of the forming power on existing machines can be easily implemented by installing a flow rate and pressure meter at the gas entrance, and if necessary, exits of the part. An important additional benefit is that a machine thus equipped can function as an online rheometer that can characterize the viscosity of the material at the operating point by tuning the simulation to the online measurements.

In 2017 the National Research Council of Canada developed an evaporation model for controlling engine icing tunnels in real time. The model included simplifications to allow it to update the control system once per second, including the assumption of sea level pressure in some calculations. Recently the engine icing system was required in an altitude facility requiring operation down to static temperatures of -40°C, and up to an altitude of 9.1 km (30 kft) or 30 kPa. To accommodate the larger temperature and pressure range the model was modified by removing the assumption of sea level operation and expanding the temperature range. In addition, due to the higher concentration of water vapor that can be held by the atmosphere at lower pressures, the significance of the effect of humidity on the air properties and the effect on the model was investigated.

Strain controlled thermo-mechanical fatigue (TMF) tests were conducted on a high Silicon ductile cast iron (SiMo) as the baseline material and a similar SiMo cast iron with aluminum addition (SiMoAl). The much improved fatigue life with aluminum addition is analyzed using the integrated creep-fatigue theory (ICFT) in combination with the metallurgical analysis on the tested coupons. Addition of about 3 wt.% Aluminum significantly improved TMF life of the SiMo cast iron. The results are explained by elimination of brittleness at middle temperature range, the higher flow stress, lower creep rate and higher oxidation resistance from Al addition.

A two-truck platoon based on a prototype cooperative adaptive cruise control (CACC) system was tested on a closed test track in a variety of realistic traffic and transient operating scenarios - conditions that truck platoons are likely to face on real highways. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate, serving as proxies to evaluate the impact of aerodynamic drag reduction under constant-speed conditions. These measurements demonstrate the effects of: the presence of a multiple-passenger-vehicle pattern ahead of and adjacent to the platoon, cut-in and cut-out manoeuvres by other vehicles, transient traffic, the use of mismatched platooned vehicles (van trailer mixed with flatbed trailer), and the platoon following another truck with adaptive cruise control (ACC).

Fuel savings from truck platooning are generally attributed to an aerodynamic drag-reduction phenomena associated with close-proximity driving. The current paper is the third in a series of papers documenting track testing of a two-truck platoon with a Cooperative Adaptive Cruise Control (CACC) system where fuel savings and aerodynamics measurements were performed simultaneously. Constant-speed road-load measurements from instrumented driveshafts and on-board wind anemometry were combined with vehicle measurements to calculate the aerodynamic drag-area of the vehicles. The drag-area results are presented for each vehicle in the two-truck platoon, and the corresponding drag-area reductions are shown for a variety of conditions: gap separation distances (9 m to 87 m), lateral offsets (up to 1.3 m), dry-van and flatbed trailers, and in the presence of surrounding traffic.

Road vehicles in the real world experience aerodynamic conditions that might be unappreciated and omitted in wind-tunnel experiments or in numerical simulations. Precipitation can potentially have an impact on the aerodynamics of road vehicles. An experimental study was devised to measure, in a wind tunnel, the impact of rain on the aerodynamic forces of the DrivAer research model. In this study, a rain system was commissioned to simulate natural rain in a wind-tunnel environment for full-scale rain rates between about 8 and 250 mm/hr. A 30%-scale DrivAer model was tested with and without precipitation for two primary configurations: the notch-back and estate-back variants. In addition, mirror-removal and covered-wheel-well configurations were investigated. The results demonstrate a distinct relationship between increasing rain intensities and increased drag of the model, providing evidence that road vehicles experience higher drag when travelling in precipitation conditions.