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Platooning is a coordinated driving strategy by which following trucks are placed into the wake of leading vehicles. Doing this leads to two primary benefits. First, the vehicles following are shielded from aerodynamic drag by a “pulling” effect. Secondly, by placing vehicles behind the leading truck, the leading vehicles experience a “pushing” effect. The reduction in aerodynamic drag leads to reduced fuel usage and, consequently, reduced greenhouse gas emissions. To maximize these effects, the inter-vehicle distance, or headway, needs to be minimized. In current platooning strategy iterations, Coordinated Adaptive Cruise Control (CACC) is used to maintain close following distances. Many of these strategies utilize the fuel rate signal as a controller cost function parameter. By using fuel rate, current control strategies have limited applicability to non-conventional powertrains.

Platooning vehicles present novel pathways to saving fuel during transportation. With the rise of autonomous solutions, platooning becomes an increasingly apparent sector requiring the application of this new technology. Platooning vehicles travel together intending to reduce aerodynamic resistance during operation. Drafting allows following vehicles to increase fuel economy and save money on refueling, whether that be at the pump or at a charging station. However, autonomous solutions are still in infancy, and controller evaluation is an exciting challenge proposed to researchers. This work brings forth a new application of an emissions quantification metric called vehicle-specific power (VSP). Rather than utilize its emissions investigative benefits, the present work applies VSP to heterogeneous Class 8 Heavy-Duty truck platoons as a means of evaluating the efficacy of Cooperative Adaptive Cruise Control (CACC).

Platooning is a promising technology which can mitigate greenhouse gas impacts and reduce transportation energy consumption. Platooning is a coordinated driving strategy where trucks align themselves in order to realize aerodynamic benefits to reduce required motive force. The aerodynamic benefit is seen as either a “pull” effect experienced by the following vehicles or a “push” effect experienced by the leader. The energy savings magnitude increases nonlinearly as headway (following distance) is reduced [1]. In efforts to maximize energy savings, cooperative adaptive cruise control (CACC) is utilized to maintain relatively short headways. However, when platooning is attempted in the real world, small transient accelerations caused by imperfect control result in observed energy savings being less than expected values. This study analyzes the performance of a recently developed nonlinear model predictive control (NMPC) platooning strategy over challenging terrain.

Platooning has produced significant energy savings for vehicles in a controlled environment. However, the impact of real-world disturbances, such as grade and interactions with passenger vehicles, has not been sufficiently characterized. Follower vehicles in a platoon operate with both different aerodynamic drag and different velocity traces than while driving alone. While aerodynamic drag reduction usually dominates the change in energy consumption for platooning vehicles, the dynamics imposed on the follow vehicle by the lead vehicle and exogenous disturbances impacting the platoon can negate aerodynamic energy savings. In this paper, a methodology is proposed to link the change in longitudinal platooning dynamics with the energy consumption of a platoon follower in real time. This is accomplished by subtracting a predicted acceleration from measured longitudinal acceleration.

Steering actuator lag is detrimental to the performance of lateral control systems and often leads to oscillation, reduced stability margins, and in some cases, instability. If the actuator lag is significant, compensation is required to maintain stability and meet performance specifications. Many recent works use a high-level approach to compensate for delay by utilizing model-based methods such as model predictive control (MPC). While these methods are effective when accurate models of both the vehicle and the actuator are available, they are susceptible to model errors. This work presents a low-level, adaptive control architecture to compensate for unknown or varying steering delay and dynamics. Using an inner-loop controller to regulate steer angle commands, oscillation can be reduced, and stability margins can be maintained without the need for an accurate vehicle model.

Platooning heavy-duty trucks decreases aerodynamic drag for following trucks, reducing energy consumption, and increasing both range and mileage. Previous platooning experimentation has demonstrated fuel economy benefits in two-, three-, and four-truck configurations. However, exogenous variables disturb the ability of these platoons to maintain the desired formation, causing an accordion effect within the platoon and reducing energy benefits via acceleration/deceleration events. This phenomenon is increasingly exacerbated as platoon size and road grade variations increase. The current work assesses how platoon size, road curvature, and road grade influence platoon energy efficiency. Fuel consumption rate is experimentally quantified for four heterogeneous Class 8 vehicles operating in standalone (baseline), two-, and four-truck platooning configurations to assess fuel consumption changes while driving through diverse road conditions.

This paper will describe the development of a load estimation algorithm that is used to estimate the load parameters necessary to detect a vehicle’s proximity to rollover. When operating a vehicle near its handling limits or with large loads, vehicle rollover must be considered for safe operation. Vehicle mass and center of gravity (CG) height play a large role in a vehicle’s rollover propensity. Cargo and passenger vehicles operate under a range of load configurations; therefore, changes in load should be estimated. Researchers have often developed load estimation and rollover detection algorithms separately. This paper will develop a load estimation algorithm and use the load estimates and vehicle states to detect rollover. The load estimation algorithm uses total least squares and is broken into two parts. First, mass is estimated based on a “full-car” dynamic ride model. Next, the CG height and inertia are estimated using the previously estimated mass and a dynamic roll model.

Semi-trucks, specifically class-8 trucks, have recently become a platform of interest for autonomy systems. Platooning involves multiple trucks following each other in close proximity, with only the lead truck being manually driven and the rest being controlled autonomously. This approach to semi-truck autonomy is easily integrated on existing platforms, reduces delivery times, and reduces greenhouse gas emissions via fuel economy benefits. Level 1 SAE fuel studies were performed on class-8 trucks operating with the Auburn Cooperative Adaptive Cruise Control (CACC) system, and fuel savings up to 10-12% were seen. Enabling platooning autonomy required the use of radar, global positioning systems (GPS), and wireless vehicle-to-vehicle (V2V) communication. Poor measurements and state estimates can lead to incorrect or missing positioning data, which can lead to unnecessary dynamics and finally wasted fuel.

Platooning has the potential to reduce greenhouse gas emissions of heavy-duty vehicles. Prior platooning studies have chiefly focused on the fuel economy characteristics of two- and three-truck platoons, and most have investigated aerodynamically homogeneous platoons with trucks of the same trim. For real world application and accurate return on investment for potential adopters, non-uniform platoons and the impacts of grade and disturbances on a platoon’s fuel economy must also be characterized. This study investigates the fuel economy of a heterogeneous four-truck platoon on a closed test track. Tests were run for one hour at a speed of 45 mph. The trucks used for this study are two 2015 Peterbilt 579’s with a Cummins ISX15 and a Paccar MX-13, and two 2009 Freightliner M915A5’s, one armored and the other unarmored. Many analysis methodologies were leveraged to describe and compare the fuel data, including lap-wise and track-segment analysis.

Presently, a main mobility sector objective is to reduce its impact on the global greenhouse gas emissions. While there are many techniques being explored, a promising approach to improve fuel economy is to reduce the required energy by using slipstream effects. This study analyzes the demanded engine power and mechanical energy used by heavy-duty trucks during platooning and non-platooning operation to determine the aerodynamic benefits of the slipstream. A series of platooning tests utilizing class 8 semi-trucks platooning via Cooperative Adaptive Cruise Control (CACC) are performed. Comparing the demanded engine power and mechanical energy used reveals the benefits of platooning on the aerodynamic drag while disregarding any potential negative side effects on the engine. However, energy savings were lower than expected in some cases.

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.

As global navigation satellite systems (GNSS) such as GPS have grown more pervasive, the use of GNSS to automatically control ground vehicles has drawn increasing interest. This cutting-edge resource offers you a thorough understanding of this emerging application area of GNSS. Written by highly-regarded authorities in the field, this unique reference covers a wide range of key topics, including ground vehicles models, psuedolites, highway vehicle control, unmanned ground vehicles, farm tractors, and construction equipment. The book is supported with over 150 illustrations and more than 180 equations.

This paper investigates the use of GPS to estimate vehicle sideslip and tire information. Both a one antenna GPS antenna/receiver and dual GPS antenna method are studied. Analysis of the accuracy that can be achieved using the two different GPS solutions is provided. The algorithms are then validated on a fully instrumented Infiniti G35 sedan. Experimental data is given showing the performance of the GPS based sideslip estimates compared against a simple bicycle model and a Datron™ velocity sensor.

This paper investigates the effect of various vehicle parameters on rollover propensity using computer simulation. The computer simulation’s accuracy is verified by comparing it to experimental data from NHTSA’s Phase IV testing on rollover of passenger vehicles. The vehicle model used in the simulation study considers the non-linear, transient dynamics of both yaw and roll motion. The vehicle model is subjected to a specific steering input defined by NHTSA, the Fishhook 1a. A correlation between the vehicle parameter of center of gravity location and rollover propensity is found using the validated vehicle simulation.

This paper evaluates the use of a low cost inertial navigation system (INS) combined with Carrier-Phase Differential GPS (DGPS), to provide continuous position and attitude estimation for the control of a farm tractor. The INS system is used for dead-reckoning navigation to control the vehicle through short GPS outages. An Extended Kalman filter combines INS and Doppler radar measurements with cm-level Carrier-Phase Differential GPS measurements for continuous position and attitude estimation of the tractor. Results are given which verify the ability of the INS system to provide a heading accuracy within ±0.6° for control of the tractor. Additionally it is shown that the dead-reckoning system can provide position and attitude estimation to control the tractor to within ±0.3 meters through a short GPS outage.