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Truck platooning is an emerging technology that exploits the drag reduction experienced by bluff bodies moving together in close longitudinal proximity. The drag-reduction phenomenon is produced via two mechanisms: wake-effect drag reduction from leading vehicles, whereby a following vehicle operates in a region of lower apparent wind speed, thus reducing its drag; and base-drag reduction from following vehicles, whereby the high-pressure field forward of a closely-following vehicle will increase the base pressure of a leading vehicle, thus reducing its drag. This paper presents a physics-guided empirical model for calculating the drag-reduction benefits from truck platooning. The model provides a general framework from which the drag reduction of any vehicle in a heterogeneous truck platoon can be calculated, based on its isolated-vehicle drag-coefficient performance and limited geometric considerations.

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.

Road-vehicle platooning is known to reduced aerodynamic drag. Recent aerodynamic-platooning investigations have suggested that follower-vehicle drag-reduction benefits persist to large, safe inter-vehicle driving distances experienced in everyday traffic. To investigate these traffic-wake effects, a wind-tunnel wake-generator system was designed and used for aerodynamic-performance testing with light-duty-vehicle (LDV) and heavy-duty-vehicle (HDV) models. This paper summarizes the development of this Road Traffic and Turbulence System (RT2S), including the identification of typical traffic-spacing conditions, and documents initial results from its use with road-vehicle models. Analysis of highway-traffic-volume data revealed that, in an uncongested urban-highway environment, the most-likely condition is a speed of 105 km/h with an inter-vehicle spacing of about 50 m.

The authors present transient wind velocity measurements from two successive, well-documented truck platooning track-test campaigns to assess the wake-shedding behavior experienced by trucks in various platoon formations. Utilizing advanced analytics of data from fast-response (100-200-Hz) multi-hole pressure probes, this analysis examines aerodynamic flow features and their relationship to energy savings during close-following platoon formations. Applying Spectral analysis to the wind velocity signals, we identify the frequency content and vortex-shedding behavior from a forward truck trailer, which dominates the flow field encountered by the downstream trucks. The changes in dominant wake-shedding frequencies correlate with changes to the lead and follower truck fuel savings at short separation distances.

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the thermal control/heat rejection system sensitivity to intentional lateral offsets over a range of intervehicle spacings. Previous studies have shown the following vehicle can experience elevated temperatures and reduced airflow through the cooling package as a result of close-formation platooning. Four anemometers positioned across the grille of the following trucks as well as aligned and multiple offset positions are used to evaluate the sensitivity of the impact. Straight sections of the track are isolated for the most accurate airflow impact measurements and to be most representative of on-highway driving. An intentional lateral offset in truck platooning is considered as a controls approach to mitigate reduced cooling efficacy at close following scenarios where the highest platoon savings are achieved.

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.

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of intentional lateral offsets over a range of intervehicle spacings. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb. In addition, the SAE J1939 instantaneous fuel rate was calibrated against the gravimetric measurements and used as proxy for additional analyses. The testing campaign demonstrated the effects of intervehicle gaps, following-vehicle longitudinal control, and manual lateral control. The new results are compared to previous truck-platooning studies to reinforce the value of the new information and demonstrate similarity to past trends. Fuel savings for the following vehicle was observed to exceed 10% at closer following distances.

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 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).

Recent research to investigate the aerodynamic-drag reduction associated with truck platooning systems has begun to reveal that surrounding traffic has a measurable impact on the aerodynamic performance of heavy trucks. A 1/15-scale wind-tunnel study was undertaken to measure changes to the aerodynamic drag experienced by heavy trucks in the presence of upstream traffic. The results, which are based on traffic conditions with up to 5 surrounding vehicles in a 2-lane configuration and consisting of 3 vehicle shapes (compact sedans, SUVs, and a medium-duty truck), show drag reductions of 1% to 16% for the heavy truck model, with the largest reductions of the same order as those experienced in a truck-platooning scenario. The data also reveal that the performance of drag-reduction technologies applied to the heavy-truck model (trailer side-skirts and a boat-tail) demonstrate different performance when applied to an isolated vehicle than to conditions with surrounding traffic.

With increasing use of boat-tails on Canadian roads, a concern had been raised regarding the possibility for ice and snow to accumulate and shed from the cavity of a boat-tail affixed to a dry-van trailer, posing a hazard for other road users. This paper describes a preliminary evaluation of the potential for ice and snow accumulation in the cavity of a boat-tail-equipped heavy-duty vehicle. A transient CFD approach was used and combined with a quasi-static particle-tracking simulation to evaluate, firstly, the tendency of various representative ice or snow particles to be entrained in the vehicle wake, and secondly, the potential of such particles to accumulate on the aft end of a dry-van trailer with and without various boat-tail configurations. Results of the particle tracking analyses showed that the greatest numbers of particles impinge on the base of the trailer for the no-boat-tail case, concentrated on the upper surface of the back face of the trailer.

During the past year, a novel turbulence generation system has been commissioned in the National Research Council (NRC) 9 m Wind Tunnel. This system, called the Road Turbulence System was developed to simulate with high fidelity the turbulence experienced by a heavy duty vehicle on the road at a geometrical scale of 30%. The turbulence characteristics that it can simulate were defined based on an extensive field measurement campaign on Canadian roads for various conditions (heavy and light traffic, topography, exposure) at heights above ground relevant not only for heavy duty vehicles but also for light duty vehicles. In an effort to improve continually the simulation of the road conditions for aerodynamic evaluations of ground vehicles, a study was carried out at NRC to define the applicability of the Road Turbulence System to aerodynamic testing of full-scale light duty vehicles.

Terrestrial winds play an important role in affecting the aerodynamics of road vehicles. Of increasing importance is the effect of the unsteady turbulence structure of these winds and their influence on the process of optimizing aerodynamic performance to reduce fuel consumption. In an effort to predict better the aerodynamic performance of heavy-duty vehicles and various drag reduction technologies, a study was undertaken to measure the turbulent wind characteristics experienced by heavy-duty vehicles on the road. To measure the winds experienced on the road, a sport utility vehicle (SUV) was outfitted with an array of four fast-response pressure probes that could be arranged in vertical or horizontal rake configurations that provided measurements up to 4.0 m from the ground and spanning a width of 2.4 m. To characterize the influence of the proximity of the vehicle on the pressure signals of the probes, the SUV and its measurements system was calibrated in a large wind tunnel.

The 9-meter wind tunnel of the National Research Council (NRC) of Canada is equipped with a boundary layer suction system, center belt and wheel rollers to simulate ground motion relative to test articles. Although these systems were originally commissioned for testing of full-scale automotive models, they are appropriately sized for ground simulation with half-scale tractor-trailer combinations. The size of the tunnel presents an opportunity to test half-scale commercial vehicles at full-scale Reynolds numbers with a model that occupies 3% of the test section cross-sectional area. This study looks at the effects of ground simulation on the force and pressure data of a half-scale model with rotating tractor wheels. A series of model changes, typical of a drag reduction program, were undertaken and each configuration was tested with both a fixed floor and with full-ground simulation to evaluate the effects of this technology on the total and incremental drag coefficients.