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\n\n \n \n \n \n \n \n Design and Implementation of an SAE Level-2 Lane Keeping System for Class 8 Trucks Using Nonlinear Model Predictive Control.\n \n \n \n \n\n\n \n Ward, J. W.; Pierce, J. D.; Brown, L.; and Bevly, D. M.\n\n\n \n\n\n\n In August 2023. IEEE\n
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Http://spot.lib.auburn.edu/login?url\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inproceedings{ward_design_2023,\n\ttitle = {Design and {Implementation} of an {SAE} {Level}-2 {Lane} {Keeping} {System} for {Class} 8 {Trucks} {Using} {Nonlinear} {Model} {Predictive} {Control}},\n\turl = {http://spot.lib.auburn.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edseee&AN=edseee.10253308&site=eds-live&scope=site},\n\tabstract = {This paper focuses on the design and evaluation of a lateral Nonlinear Model Predictive Control (NMPC) path following algorithm for Class 8 vehicles. While NMPC allows for the inclusion of constraints such as obstacle avoidance or rollover prevention, this work is primarily focused on testing the path following capabilities of the NMPC controller without the consideration of constraints. Although many lateral controllers for tractor-trailer systems use either fully linear or fully nonlinear Equations of Motion (EOMs), this paper presents a hybrid model. This hybrid model uses a linear model to describe the dynamics of the tractor-trailer while using nonlinear equations to describe the global position of the system. This model is then implemented in a NMPC architecture. The controller is first designed and tested in MATLAB to yield a controller that can follow a lane change maneuver. This controller is then implemented in a real-time format using ROS and C++. The objective of the real-time controller was to replicate the path of a Kia Optima which was driving in front of the test vehicle. To accomplish this, an estimator was developed to calculate the relative path between the vehicles with enough accuracy to stay within the standard road lane. This estimation algorithm was able to produce lateral position errors with a standard deviation of 1.96 cm. Finally, the lateral controller was shown to track the generated reference path with a mean error of 1.47 cm and a RMS error of under 27 cm.},\n\tpublisher = {IEEE},\n\tauthor = {Ward, Jacob W. and Pierce, J. Daniel and Brown, Lowell and Bevly, David M.},\n\tmonth = aug,\n\tyear = {2023},\n}\n\n\n\n
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\n This paper focuses on the design and evaluation of a lateral Nonlinear Model Predictive Control (NMPC) path following algorithm for Class 8 vehicles. While NMPC allows for the inclusion of constraints such as obstacle avoidance or rollover prevention, this work is primarily focused on testing the path following capabilities of the NMPC controller without the consideration of constraints. Although many lateral controllers for tractor-trailer systems use either fully linear or fully nonlinear Equations of Motion (EOMs), this paper presents a hybrid model. This hybrid model uses a linear model to describe the dynamics of the tractor-trailer while using nonlinear equations to describe the global position of the system. This model is then implemented in a NMPC architecture. The controller is first designed and tested in MATLAB to yield a controller that can follow a lane change maneuver. This controller is then implemented in a real-time format using ROS and C++. The objective of the real-time controller was to replicate the path of a Kia Optima which was driving in front of the test vehicle. To accomplish this, an estimator was developed to calculate the relative path between the vehicles with enough accuracy to stay within the standard road lane. This estimation algorithm was able to produce lateral position errors with a standard deviation of 1.96 cm. Finally, the lateral controller was shown to track the generated reference path with a mean error of 1.47 cm and a RMS error of under 27 cm.\n
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\n\n \n \n \n \n \n \n The Utilization of Geometric Hashing Techniques for Feature Association during Ground Vehicle Localization. [electronic resource].\n \n \n \n \n\n\n \n Sprunk, M.; Bevly, D. M.; Martin, S. M.; and Oeding, L. A.\n\n\n \n\n\n\n Ph.D. Thesis, 2023.\n
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@phdthesis{sprunk_utilization_2023,\n\ttitle = {The {Utilization} of {Geometric} {Hashing} {Techniques} for {Feature} {Association} during {Ground} {Vehicle} {Localization}. [electronic resource]},\n\turl = {https://etd.auburn.edu//handle/10415/8682},\n\tauthor = {Sprunk, Michael and Bevly, David M. and Martin, Scott M. and Oeding, Luke A.},\n\tyear = {2023},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n Timing Evaluation of Iridium Satellite Time and Location Signal. [electronic resource] : Measurement-Level Implementation and Receiver Hardware Time Interval Comparison.\n \n \n \n \n\n\n \n Smith, A. M.; Bevly, D. M.; Martin, S. M.; and Rose, C. A.\n\n\n \n\n\n\n Ph.D. Thesis, 2023.\n
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@phdthesis{smith_timing_2023,\n\ttitle = {Timing {Evaluation} of {Iridium} {Satellite} {Time} and {Location} {Signal}. [electronic resource] : {Measurement}-{Level} {Implementation} and {Receiver} {Hardware} {Time} {Interval} {Comparison}.},\n\turl = {https://etd.auburn.edu//handle/10415/9082},\n\tauthor = {Smith, Austin M. and Bevly, David M. and Martin, Scott M. and Rose, Chad A.},\n\tyear = {2023},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n Decentralized Collaborative Navigation in Limited Observability Environments with Low Earth Orbit Satellite Signals of Opportunity between Aerial and Ground Vehicles. [electronic resource].\n \n \n \n \n\n\n \n Moomaw, C. D.; Martin, S. M.; Bevly, D. M.; and Rose, C. A.\n\n\n \n\n\n\n Ph.D. Thesis, 2023.\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@phdthesis{moomaw_decentralized_2023,\n\ttitle = {Decentralized {Collaborative} {Navigation} in {Limited} {Observability} {Environments} with {Low} {Earth} {Orbit} {Satellite} {Signals} of {Opportunity} between {Aerial} and {Ground} {Vehicles}. [electronic resource]},\n\turl = {https://etd.auburn.edu//handle/10415/8839},\n\tauthor = {Moomaw, Christian D. and Martin, Scott M. and Bevly, David M. and Rose, Chad A.},\n\tyear = {2023},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n A Software Signal Simulation of Low Earth Orbit Satellites for Investigative Analysis. [electronic resource].\n \n \n \n \n\n\n \n McDougal, S. A.; Martin, S. M.; Bevly, D. M.; and Allen, B.\n\n\n \n\n\n\n Ph.D. Thesis, 2023.\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@phdthesis{mcdougal_software_2023,\n\ttitle = {A {Software} {Signal} {Simulation} of {Low} {Earth} {Orbit} {Satellites} for {Investigative} {Analysis}. [electronic resource]},\n\turl = {https://etd.auburn.edu//handle/10415/8697},\n\tauthor = {McDougal, Samuel A. and Martin, Scott M. and Bevly, David M. and Allen, Brendon},\n\tyear = {2023},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n Adaptive Steering Actuator Delay Compensation for a Vehicle Lateral Control System. [electronic resource].\n \n \n \n \n\n\n \n Kennedy, W.; Bevly, D. M.; Martin, S. M.; and Allen, B.\n\n\n \n\n\n\n Ph.D. Thesis, 2023.\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@phdthesis{kennedy_adaptive_2023,\n\ttitle = {Adaptive {Steering} {Actuator} {Delay} {Compensation} for a {Vehicle} {Lateral} {Control} {System}. [electronic resource]},\n\turl = {https://etd.auburn.edu//handle/10415/8690},\n\tauthor = {Kennedy, William and Bevly, David M. and Martin, Scott M. and Allen, Brendon},\n\tyear = {2023},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n A Loosely Coupled GNSS/PDR Integration Approach for Pedestrian Navigation. [electronic resource].\n \n \n \n \n\n\n \n Jones, C. S.; Bevly, D. M.; Chen, H.; and Rose, C. A.\n\n\n \n\n\n\n Ph.D. Thesis, 2023.\n
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@phdthesis{jones_loosely_2023,\n\ttitle = {A {Loosely} {Coupled} {GNSS}/{PDR} {Integration} {Approach} for {Pedestrian} {Navigation}. [electronic resource]},\n\turl = {https://etd.auburn.edu//handle/10415/8840},\n\tauthor = {Jones, Connor Steele and Bevly, David M. and Chen, Howard and Rose, Chad A.},\n\tyear = {2023},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n A GPS L1 and Cellular 4G LTE Vector Tracking Software-Defined Receiver. [electronic resource].\n \n \n \n \n\n\n \n Morgan, S. C.; Martin, S. M.; Bevly, D. M.; and Tugnait, J. K.\n\n\n \n\n\n\n Ph.D. Thesis, 2023.\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@phdthesis{morgan_gps_2023,\n\ttitle = {A {GPS} {L1} and {Cellular} {4G} {LTE} {Vector} {Tracking} {Software}-{Defined} {Receiver}. [electronic resource]},\n\turl = {https://etd.auburn.edu//handle/10415/9005},\n\tauthor = {Morgan, Samuel C. and Martin, Scott M. and Bevly, David M. and Tugnait, Jitendra K.},\n\tyear = {2023},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n Adaptive Actuator Delay Compensation for a Vehicle Lateral Control System.\n \n \n \n \n\n\n \n Kennedy, W. T.; and Bevly, D. M.\n\n\n \n\n\n\n In pages 2023–01–0677, Detroit, Michigan, United States, April 2023. \n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inproceedings{kennedy_adaptive_2023,\n\taddress = {Detroit, Michigan, United States},\n\ttitle = {Adaptive {Actuator} {Delay} {Compensation} for a {Vehicle} {Lateral} {Control} {System}},\n\turl = {https://www.sae.org/content/2023-01-0677},\n\tdoi = {10.4271/2023-01-0677},\n\tabstract = {{\\textless}div class="section abstract"{\\textgreater}{\\textless}div class="htmlview paragraph"{\\textgreater}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. The Smith Predictor (SP) control scheme is implemented in the inner-loop to mitigate the effects of the communication delay between the controller and the steering actuator. An algorithm will be presented to estimate both the communication delay between the controller and actuator and the steering dynamics. These estimates will be used to adapt the inner-loop SP to maintain gain and phase margins while reducing oscillation. Estimating the steering lag allows the algorithm to compensate for unknown or changing steering dynamics and communication delay. Results are presented both from simulation and from real-time experiments on a vehicle outfitted with drive-by-wire (DBW) hardware.{\\textless}/div{\\textgreater}{\\textless}/div{\\textgreater}},\n\tlanguage = {en},\n\turldate = {2024-02-07},\n\tauthor = {Kennedy, William Thomas and Bevly, David M.},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {2023--01--0677},\n}\n\n\n\n
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\n \\textlessdiv class=\"section abstract\"\\textgreater\\textlessdiv class=\"htmlview paragraph\"\\textgreaterSteering 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. The Smith Predictor (SP) control scheme is implemented in the inner-loop to mitigate the effects of the communication delay between the controller and the steering actuator. An algorithm will be presented to estimate both the communication delay between the controller and actuator and the steering dynamics. These estimates will be used to adapt the inner-loop SP to maintain gain and phase margins while reducing oscillation. Estimating the steering lag allows the algorithm to compensate for unknown or changing steering dynamics and communication delay. Results are presented both from simulation and from real-time experiments on a vehicle outfitted with drive-by-wire (DBW) hardware.\\textless/div\\textgreater\\textless/div\\textgreater\n
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\n\n \n \n \n \n \n \n New Controller Evaluation Techniques for Autonomously Driven Heavy-Duty Convoys.\n \n \n \n \n\n\n \n Snitzer, P.; Stegner, E.; Bentley, J.; Bevly, D. M.; and Hoffman, M.\n\n\n \n\n\n\n In pages 2023–01–0688, Detroit, Michigan, United States, April 2023. \n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inproceedings{snitzer_new_2023,\n\taddress = {Detroit, Michigan, United States},\n\ttitle = {New {Controller} {Evaluation} {Techniques} for {Autonomously} {Driven} {Heavy}-{Duty} {Convoys}},\n\turl = {https://www.sae.org/content/2023-01-0688},\n\tdoi = {10.4271/2023-01-0688},\n\tabstract = {{\\textless}div class="section abstract"{\\textgreater}{\\textless}div class="htmlview paragraph"{\\textgreater}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). VSP creates a bridge between types of passenger vehicles to compare emission rates via estimating powertrain effort to maintain current conditions (speed, acceleration, road grade, etc.). In this study, different controller strategies and platoon configurations are examined to determine the applicability of VSP to controller evaluation. Experiments were completed at the National Center for Asphalt Technology (NCAT) circuitous track, the American Center for Mobility’s (ACM) freeway loop, and a straight section of NCAT’s track dubbed “ideal” for platooning efficiency. One truck is analyzed and compared to a lead truck, where VSP traces are calculated at each time step of experimentation. The influence of road grade, platoon size, and platooning position is considered in this study. Because the calculation of VSP considers an isolated driving environment, it effectively assesses the controller’s ability to reduce energy consumption for platooning vehicles.{\\textless}/div{\\textgreater}{\\textless}/div{\\textgreater}},\n\tlanguage = {en},\n\turldate = {2024-02-07},\n\tauthor = {Snitzer, Philip and Stegner, Evan and Bentley, John and Bevly, David M. and Hoffman, Mark},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {2023--01--0688},\n}\n\n\n\n\n\n\n\n
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\n \\textlessdiv class=\"section abstract\"\\textgreater\\textlessdiv class=\"htmlview paragraph\"\\textgreaterPlatooning 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). VSP creates a bridge between types of passenger vehicles to compare emission rates via estimating powertrain effort to maintain current conditions (speed, acceleration, road grade, etc.). In this study, different controller strategies and platoon configurations are examined to determine the applicability of VSP to controller evaluation. Experiments were completed at the National Center for Asphalt Technology (NCAT) circuitous track, the American Center for Mobility’s (ACM) freeway loop, and a straight section of NCAT’s track dubbed “ideal” for platooning efficiency. One truck is analyzed and compared to a lead truck, where VSP traces are calculated at each time step of experimentation. The influence of road grade, platoon size, and platooning position is considered in this study. Because the calculation of VSP considers an isolated driving environment, it effectively assesses the controller’s ability to reduce energy consumption for platooning vehicles.\\textless/div\\textgreater\\textless/div\\textgreater\n
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\n\n \n \n \n \n \n \n Quantifying the Energy Impact of Autonomous Platooning-Imposed Longitudinal Dynamics.\n \n \n \n \n\n\n \n Stegner, E.; Snitzer, P.; Bentley, J.; Bevly, D. M.; and Hoffman, M.\n\n\n \n\n\n\n In pages 2023–01–0896, Detroit, Michigan, United States, April 2023. \n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inproceedings{stegner_quantifying_2023,\n\taddress = {Detroit, Michigan, United States},\n\ttitle = {Quantifying the {Energy} {Impact} of {Autonomous} {Platooning}-{Imposed} {Longitudinal} {Dynamics}},\n\turl = {https://www.sae.org/content/2023-01-0896},\n\tdoi = {10.4271/2023-01-0896},\n\tabstract = {{\\textless}div class="section abstract"{\\textgreater}{\\textless}div class="htmlview paragraph"{\\textgreater}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. The real-time consumption calculation methodology is evaluated using data from simulated and experimental platoons. The proposed methodology allows active deceleration losses to be calculated for a platoon follower in real time and is a development of the active deceleration theory presented by the authors in SAE Paper 2022-01-0526. In simulation, energy losses calculated by the method were within 5\\% of the true value and were robust to errors in modeled aerodynamic drag. As for the experimental results, the method agreed with the prior procedure of SAE Paper 2022-01-0526, which required extensive datasets and could only be completed as a post-processing routine. This novel methodology provides an important new feedback metric for platoon operators, and makes it possible to analyze real-time platooning benefit while the platoon is on the road.{\\textless}/div{\\textgreater}{\\textless}/div{\\textgreater}},\n\tlanguage = {en},\n\turldate = {2024-02-07},\n\tauthor = {Stegner, Evan and Snitzer, Philip and Bentley, John and Bevly, David M. and Hoffman, Mark},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {2023--01--0896},\n}\n\n\n\n\n\n\n\n
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\n \\textlessdiv class=\"section abstract\"\\textgreater\\textlessdiv class=\"htmlview paragraph\"\\textgreaterPlatooning 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. The real-time consumption calculation methodology is evaluated using data from simulated and experimental platoons. The proposed methodology allows active deceleration losses to be calculated for a platoon follower in real time and is a development of the active deceleration theory presented by the authors in SAE Paper 2022-01-0526. In simulation, energy losses calculated by the method were within 5% of the true value and were robust to errors in modeled aerodynamic drag. As for the experimental results, the method agreed with the prior procedure of SAE Paper 2022-01-0526, which required extensive datasets and could only be completed as a post-processing routine. This novel methodology provides an important new feedback metric for platoon operators, and makes it possible to analyze real-time platooning benefit while the platoon is on the road.\\textless/div\\textgreater\\textless/div\\textgreater\n
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\n\n \n \n \n \n \n \n Real-Time Graph-Based Path Planning for Autonomous Racecars. [electronic resource].\n \n \n \n \n\n\n \n Keefer, S. E.; and Bevly, D. M.\n\n\n \n\n\n\n Ph.D. Thesis, 2023.\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@phdthesis{keefer_real-time_2023,\n\ttitle = {Real-{Time} {Graph}-{Based} {Path} {Planning} for {Autonomous} {Racecars}. [electronic resource]},\n\turl = {https://etd.auburn.edu//handle/10415/8803},\n\tauthor = {Keefer, Sarah Elizabeth and Bevly, David M.},\n\tyear = {2023},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n Semi-autonomous Truck Platooning with a Lean Sensor Package.\n \n \n \n \n\n\n \n Lakshmanan, S.; Adam, C.; Kleinow, T.; Richardson, P.; Ward, J.; Stegner, E.; Bevly, D.; and Hoffman, M.\n\n\n \n\n\n\n In Murphey, Y. L.; Kolmanovsky, I.; and Watta, P., editor(s),
AI-enabled Technologies for Autonomous and Connected Vehicles, of Lecture Notes in Intelligent Transportation and Infrastructure, pages 19–59. Springer International Publishing, Cham, 2023.\n
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@incollection{lakshmanan_semi-autonomous_2023,\n\taddress = {Cham},\n\tseries = {Lecture {Notes} in {Intelligent} {Transportation} and {Infrastructure}},\n\ttitle = {Semi-autonomous {Truck} {Platooning} with a {Lean} {Sensor} {Package}},\n\tisbn = {978-3-031-06780-8},\n\turl = {https://doi.org/10.1007/978-3-031-06780-8_2},\n\tabstract = {This chapter describes one method of approaching fuel-efficient truck platooning using a system called Cooperative Adaptive Cruise Control (CACC). The principal innovation in the system is its lean sensor package, including factory-ready standard ACC system utilizing a dual-beam radar, precision Global Positioning System (GPS), and a Vehicle-to-Vehicle (V2V) communication system. In other words, no imaging sensors such as camera or lidar, and no associated high-performance computing hardware such as Graphics Processing Units (GPU). Extensive test track and public road testing on class-8 semi-trucks, including edge case testing, reveals the efficacy and robustness of this system despite its leanness. Quantitative results are included in this chapter that trace cause and effect through the CACC system.},\n\tlanguage = {en},\n\turldate = {2023-06-06},\n\tbooktitle = {{AI}-enabled {Technologies} for {Autonomous} and {Connected} {Vehicles}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Lakshmanan, Sridhar and Adam, Cristian and Kleinow, Timothy and Richardson, Paul and Ward, Jacob and Stegner, Evan and Bevly, David and Hoffman, Mark},\n\teditor = {Murphey, Yi Lu and Kolmanovsky, Ilya and Watta, Paul},\n\tyear = {2023},\n\tdoi = {10.1007/978-3-031-06780-8_2},\n\tpages = {19--59},\n}\n\n\n\n
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\n This chapter describes one method of approaching fuel-efficient truck platooning using a system called Cooperative Adaptive Cruise Control (CACC). The principal innovation in the system is its lean sensor package, including factory-ready standard ACC system utilizing a dual-beam radar, precision Global Positioning System (GPS), and a Vehicle-to-Vehicle (V2V) communication system. In other words, no imaging sensors such as camera or lidar, and no associated high-performance computing hardware such as Graphics Processing Units (GPU). Extensive test track and public road testing on class-8 semi-trucks, including edge case testing, reveals the efficacy and robustness of this system despite its leanness. Quantitative results are included in this chapter that trace cause and effect through the CACC system.\n
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\n\n \n \n \n \n \n Essential PoseSLAM: An Efficient Landmark-Free Approach to Visual-Inertial Navigation.\n \n \n \n\n\n \n Boler, M.; and Martin, S.\n\n\n \n\n\n\n In
IEEE/ION PLANS 2023, April 2023. \n
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@inproceedings{boler_essential_2023,\n\ttitle = {Essential {PoseSLAM}: {An} {Efficient} {Landmark}-{Free} {Approach} to {Visual}-{Inertial} {Navigation}},\n\tabstract = {This paper presents an efficient method of fusing visual and inertial data for navigation using the two-view tensor, also known as the essential matrix. The essential matrix encodes the up-to-scale geometric relationship between two camera poses and contains the relative rotation and direction-of-translation between them. A dense network of up-to-scale relative pose measurements is constructed by computing essential matrices between incoming images and a collection of past states which observed the same scene. As the essential matrix is computed online in many visual-inertial navigation systems (VINS) as part of the image processing front end, the proposed method introduces little computational overhead while avoiding all computations related to feature estimation. This approach can be viewed as a modification of the classical pose-graph simultaneous localization and mapping (SLAM) problem. This paper further presents a dynamic initialization method to bootstrap the velocity, orientation, and biases of an IMU. The initialization method makes use of the same modified pose-graph SLAM approach to solve for the up-to-scale relative poses of a window of camera frames before solving for orientation, velocity, and sensor biases. We validate the proposed methods by implementing them in Extended Kalman Filter (EKF) and nonlinear optimization forms and testing them on public datasets.},\n\tlanguage = {en},\n\tbooktitle = {{IEEE}/{ION} {PLANS} 2023},\n\tauthor = {Boler, Matthew and Martin, Scott},\n\tmonth = apr,\n\tyear = {2023},\n}\n\n\n\n
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\n This paper presents an efficient method of fusing visual and inertial data for navigation using the two-view tensor, also known as the essential matrix. The essential matrix encodes the up-to-scale geometric relationship between two camera poses and contains the relative rotation and direction-of-translation between them. A dense network of up-to-scale relative pose measurements is constructed by computing essential matrices between incoming images and a collection of past states which observed the same scene. As the essential matrix is computed online in many visual-inertial navigation systems (VINS) as part of the image processing front end, the proposed method introduces little computational overhead while avoiding all computations related to feature estimation. This approach can be viewed as a modification of the classical pose-graph simultaneous localization and mapping (SLAM) problem. This paper further presents a dynamic initialization method to bootstrap the velocity, orientation, and biases of an IMU. The initialization method makes use of the same modified pose-graph SLAM approach to solve for the up-to-scale relative poses of a window of camera frames before solving for orientation, velocity, and sensor biases. We validate the proposed methods by implementing them in Extended Kalman Filter (EKF) and nonlinear optimization forms and testing them on public datasets.\n
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\n\n \n \n \n \n \n SNAP: A Xona Space Systems and GPS Software-Defined Receiver.\n \n \n \n\n\n \n Miller, N. S; Koza, J T.; Morgan, S. C; Martin, S. M; Neish, A.; Grayson, R.; and Reid, T.\n\n\n \n\n\n\n In
IEEE/ION PLANS 2023, April 2023. \n
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@inproceedings{miller_snap_2023,\n\ttitle = {{SNAP}: {A} {Xona} {Space} {Systems} and {GPS} {Software}-{Defined} {Receiver}},\n\tabstract = {This paper proposes the Satellite Navigation Applied Processor (SNAP), a Software Defined Receiver (SDR) for the Xona Space Systems Pulsar constellation and legacy Global Navigation Satellite Systems (GNSS). The novelty of this paper is found in the creation of an SDR that serves to explore the capabilities of a new Low Earth Orbit (LEO) satellite navigation constellation. The modularity of SNAP allows both industry professionals and students to investigate their own Pulsar and legacy GNSS signal processing techniques.},\n\tlanguage = {en},\n\tbooktitle = {{IEEE}/{ION} {PLANS} 2023},\n\tauthor = {Miller, Noah S and Koza, J Tanner and Morgan, Samuel C and Martin, Scott M and Neish, Andrew and Grayson, Robert and Reid, Tyler},\n\tmonth = apr,\n\tyear = {2023},\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
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\n This paper proposes the Satellite Navigation Applied Processor (SNAP), a Software Defined Receiver (SDR) for the Xona Space Systems Pulsar constellation and legacy Global Navigation Satellite Systems (GNSS). The novelty of this paper is found in the creation of an SDR that serves to explore the capabilities of a new Low Earth Orbit (LEO) satellite navigation constellation. The modularity of SNAP allows both industry professionals and students to investigate their own Pulsar and legacy GNSS signal processing techniques.\n
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