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The current focus in the ongoing development of autonomous driving systems (ADS) for heavy duty vehicles is that of vehicle operational safety. To this end, developers and researchers alike are working towards a complete understanding of the operating environments and conditions that autonomous vehicles are subject to during their mission. This understanding is critical to the testing and validation phases of the development of autonomous vehicles and allows for the identification of both the nominal and edge case scenarios encountered by these systems. Previous work by the authors saw the development of a comprehensive scenario generation framework to identify an operating domain specification (ODS), or external and internal conditions an autonomous driving system can expect to encounter on its mission to form critical scenario groups for autonomous vehicle testing and validating using statistical patterns, clustering, and correlation.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

Adoption of fuel cell electric vehicles (FCEV) or battery electric vehicles (BEV) in heavy-duty (HD) commercial freight transportation is hampered by difficult technoeconomic obstacles. To enable widespread deployment of electrified powertrains, fleet and operational logistics need high uptime and parity with diesel system productivity/total cost of ownership (TCO), while meeting safety compliance. Due to a mix of comparatively high powerplant and energy storage costs, high energy costs (more so for FCEV), greater weight (more so for BEV), slow refueling / recharging durations, and limited supporting infrastructure, FCEV and BEV powertrains have not seen significant uptake in the HD freight transport market. The use of dynamic wireless power transfer (DWPT) systems, consisting of inductive electrical coils on the vehicle and power source transmitting coils embedded in the roadways, may address several of these challenges.

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions.

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors.

The phenomena of global warming and climate change are encouraging more and more countries, local communities, and companies to establish carbon neutrality targets, which has very significant implications for the US trucking industry. Truck electrification helps fleets to achieve zero tailpipe emissions and macro-scale decarbonization while allowing continued business growth in response to the rapid expansion of e-commerce and shipping related to increased globalization. This paper presents an analysis of Class 8 long-haul truck electrification using a commercial vehicle electrification evaluation tool and Fleet DNA drive data. The study provides new insight into the impacts of streamlined chassis, battery energy density, and superfast charging on battery capacity needs as well as implications for payload, energy consumption, and greenhouse gas emissions for electric long-haul trucks. The study also identifies a pathway for achieving optimal long-haul truck electrification.

Reducing criteria pollutants while reducing greenhouse gases is an active area of research for commercial on-road vehicles as well as for off-road machines. The heavy duty on-road sector has moved to reducing NOx by 82.5% compared to 2010 regulations while increasing the engine useful life from 435,000 to 650,000 miles by 2027 in the United States (US). An additional certification cycle, the Low Load Cycle (LLC), has been added focusing on part load operation having tight NOx emissions levels. In addition to NOx, the total CO2 emissions from the vehicle will also be reduced for various model years. The off-road market is following with a 90% NOx reduction target compared to Tier 4 Final for 130-560 kW engines along with greenhouse gas targets that are still being established. The off-road market will also need to certify with a Low Load Application Cycle (LLAC), a version of which was proposed for evaluation in 2021.

The present study was motivated by a need to expand information for consumers offered through the FuelEconomy.Gov website. To that end, a power-based modeling approach has been used to examine the effect of steady-speed driving on estimated range for model year 2020 – 2023 battery electric vehicles (BEVs). This approach allowed rapid study of a broader range of BEV models than could be accomplished through vehicle tests. Publicly accessible certification test results and other data were used to perform a regression between cycle-average tractive power requirements and the resulting electrical power. This regression enabled estimation of electric power and energy use over a range of steady highway speeds. These analyses in turn allowed projection of vehicle range at differing speeds. The projections agree within 6% with available 65 MPH manufacturer test data.

In 2021 the Federal Aviation Administration in collaboration with the National Research Council of Canada performed research on altitude ice crystal icing of aircraft engines using the modular compressor rig, ICE-MACR, in an altitude wind tunnel. The aim of the research campaign was to address research needs related to ice crystal icing of aircraft engines outlined in FAA publication Engine Ice Crystal Icing Technology Plan with Research Needs. This paper reports the findings on ice accretion from a configuration of ICE-MACR with two compression stages. Inherent in two-stage operation is not just additional fracturing and heating by the second stage but also higher axial velocity and potentially greater centrifuging of particles. These factors influence the accretion behavior in the test article compared to single stage accretion.

As the everyday use of flying small to medium size Remotely Piloted Aircraft System (RPAS) continues to evolve, so does the need to fly them in icing environments. To investigate an RPAS’ ability to fly in these conditions, an outdoor test rig has been developed at the National Research Council Canada (NRC) in which a range of RPAS have been tested in icing environments. This rig has an available test area of 3.05 m × 3.05 m, and is 5.1 m high. An array of spray nozzles installed at the top of the test rig provides a cloud that, when operated at sub-zero temperatures, enables simulation of in-flight icing conditions. The spray cloud is calibrated to provide water concentration and drop size distributions consistent with Appendix C, freezing drizzle and freezing rain conditions.

This paper presents the adhesion strength of ice on sanded and machine-finished aluminum test coupons as measured using the National Research Council of Canada (NRC) Altitude Icing Wind Tunnel (AIWT) spin rig. This rig is used to evaluate commercial and internally-developed coatings for low-adhesion properties, and the performance of ice on aluminum is required as a baseline to compare the coatings against. The tests are performed over a range of aerodynamic and icing cloud conditions, including variations in static air temperature and exposure time (and therefore accumulated ice mass). The data analysis includes an evaluation of the uncertainty in the results based on the measured ice mass repeatability and the measured shear stress repeatability.

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.

The development and calibration of a new facility to test medium size rotors for Remotely Piloted Aircraft Systems (RPAS) under in-flight icing conditions is described. This facility has made use of a 3 m x 6 m cold room available at the NRC which includes a spray system to provide the icing cloud as well as a dedicated rotor stand assembly that incorporates a load cell and dynamometer. Calibration data of the spray drop sizes and liquid water content are provided and compared to conditions of the natural environment as detailed in icing regulations for transport category airplanes, i.e., CFR 14 Part 25 Appendix C and O. Data to examine the sensitivity of rotor performance, under a constant liquid water content to various droplet sizes are provided for a medium sized rotor. Tests have also been performed that examine the ability of the rotor to maintain predefined thrust, torque and power performance throughout an icing encounter of fixed duration.

Under contract to Transport Canada (TC) and with joint funding support from the Federal Aviation Administration (FAA), a vertical stabilizer common research model (VS-CRM) has been designed and built by the National Research Council of Canada (NRC). This model is a realistic, scaled representation of modern vertical stabilizer designs without being specific to a particular aircraft. The model was installed and tested in the NRC 3 m × 6 m Icing Wind Tunnel in late 2021/early 2022. Testing was led by APS Aviation Inc., with support from NRC and NASA, in order to observe the anti-icing fluids flow-off behavior with and without freezing or frozen precipitation during simulated take-off velocity profiles. The model dry-air aerodynamic properties were characterized using flow visualization tufts and boundary layer rakes. Using this data, a target baseline configuration was selected with a yaw angle equal to 0° and rudder deflection angle equal to -10°.

Since the introduction of ice crystal icing certification requirements [1], icing facilities have played an important role in demonstrating compliance of aircraft air data probes, engine probes, and increasingly, of turbine engines. Most sea level engine icing facilities use the freezing-out of a water spray to simulate ice crystal icing conditions encountered at altitude by an aircraft in flight. However, there are notable differences in the ice particles created by freeze-out versus those observed at altitude [2, 3, 4]. Freeze-out crystals are generally spherical as compared to altitude crystals which have variable crystalline shapes. Additionally, freeze-out particles may not completely freeze in their centres, creating a combination of super-cooled liquid and ice impacting engine hardware. An alternative method for generating ice crystals in a test facility is the grinding of ice blocks or cubes to create irregular shaped crystals.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

This article presents an autonomous steering control scheme for articulated heavy vehicles (AHVs). Despite economic and environmental benefits in freight transportation, lateral stability is always a concern for AHVs in high-speed highway operations due to their multi-unit vehicle structures, and high centers of gravity (CGs). In addition, North American harsh winter weather makes the lateral stability even more challenging. AHVs often experience amplified lateral motions of trailing vehicle units in high-speed evasive maneuvers. AHVs represent a 7.5 times higher risk than passenger cars in highway operation. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous steering control of AHVs.

The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This paper develops a long-term plan for refueling infrastructure deployment using the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy duty (HD) fuel cell electric commercial vehicles (FCEV).

Road vehicles have been shown to experience measurable changes in aerodynamic performance when travelling in everyday safe-distance driving conditions, with a major contributor being the lower effective wind speed associated with the wakes from forward vehicles. Using a novel traffic-wake-generator system, a comprehensive test program was undertaken to examine the influence of traffic wakes on the aerodynamic performance of heavy-duty vehicles (HDVs). The experiments were conducted in a large wind tunnel with four primary variants of a high-fidelity 30%-scale tractor-trailer model. Three high-roof-tractor models (conventional North-American sleeper-cab and day-cab, and a zero-emissions-cab style) paired with a standard dry-van trailer were tested, along with a low-roof day-cab tractor paired with a flat-bed trailer.

To achieve carbon neutrality by 2060, the Chinese government has put effort into decarbonizing the transportation sector. Consequently, China elaborated a new energy vehicle strategy promoting the production of electric vehicles and expanding into hydrogen (H2) vehicle technologies including fuel cell electric vehicles and H2 internal combustion engine vehicles. The Transportation Energy Analysis Model (TEAM) projects the market penetration as well as energy demand and greenhouse gas emissions in China up to 2050. By integrating the Monte Carlo simulation, this study tests the robustness of TEAM and investigates the key parameters that will shape passenger vehicle sales and emissions in the future. The results show that fuel cell cost, H2 price, and battery cost are the most sensitive parameters for H2 vehicle technologies.