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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.

This paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone “interlocking” at each junction location deconflicts railway traffic, affirming safe passage.

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other three as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method.

Cab climate conditioning is one of the primary reasons for operating the main engine in a long-haul truck during driver rest periods. In the United States, sleeper cab trucks use approximately 667 million gallons of fuel annually for rest period idling. The U.S. Department of Energy's National Renewable Energy Laboratory's (NREL) CoolCab Project works closely with industry to design efficient thermal management systems for long-haul trucks that minimize engine idling and fuel use while maintaining occupant comfort. Heat transfer to the vehicle interior from opaque exterior surfaces is one of the major heat pathways that contribute to air conditioning loads during long-haul truck daytime rest period idling. To quantify the impact of paint color and the opportunity for advanced paints, NREL collaborated with Volvo Group North America, PPG Industries, and Dometic Environmental Corporation.

The objective of the Heavy Vehicle Propulsion Materials Program is to develop the enabling materials technology for the clean, high-efficiency diesel truck engines of the future. The development of cleaner, higher-efficiency diesel engines imposes greater mechanical, thermal, and tribological demands on materials of construction. Often the enabling technology for a new engine component is the material from which the part can be made. The Heavy Vehicle Propulsion Materials Program is a partnership between the Department of Energy (DOE), and the diesel engine companies in the United States, materials suppliers, national laboratories, and universities. A comprehensive research and development program has been developed to meet the enabling materials requirements for the diesel engines of the future.

Five different commercially available high-temperature martensitic steels were evaluated for use in a heavy-duty diesel engine piston application and compared to existing piston alloys 4140 and microalloyed steel 38MnSiVS5 (MAS). Finite element analyses (FEA) were performed to predict the temperature and stress distributions for severe engine operating conditions of interest, and thus aid in the selection of the candidate steels. Complementary material testing was conducted to evaluate the properties relevant to the material performance in a piston. The elevated temperature strength, strength evolution during thermal aging, and thermal property data were used as inputs into the FEA piston models. Additionally, the long-term oxidation performance was assessed relative to the predicted maximum operating temperature for each material using coupon samples in a controlled-atmosphere cyclic-oxidation test rig.

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.

Chassis based emissions characterization of heavy-duty vehicles has advanced over the last decade, but the understanding of the effect of test schedule on measured emissions is still poor. However, this is an important issue because the test schedule should closely mimic actual vehicle operation or vocation. A wide variety of test schedules was reviewed and these cycles were classified as cycles or routes and as geometric or realistic. With support from the U.S. Department of Energy Office of Transportation Technologies (DOE/OTT), a GMC box truck with a Caterpillar 3116 engine and a Peterbilt over the road tractor-trailer with a Caterpillar 3406 engine were exercised through a large number of cycles and routes. Test weight for the GMC was 9,980 kg and for the Peterbilt was 19,050 kg. Emissions characterization was performed using a heavy-duty chassis dynamometer, with a full-scale dilution tunnel, analyzers for gaseous emissions, and filters for PM emissions.