@article{earnhardt_groelke_borek_pelletier_brennan_vermillion_2022, title={Cooperative Exchange-Based Platooning Using Predicted Fuel-Optimal Operation of Heavy-Duty Vehicles}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2022.3169390}, abstractNote={Several driving situations exist where fuel-optimal driving in terms of aggregate performance can only be achieved when one or more vehicles incurs a sacrifice in its own fuel consumption. For these situations, an economic incentive is needed to entice that vehicle to participate in aggregate fuel-optimal driving. Focusing on platooning amongst automated heavy-duty vehicles and using real trucking routes, we examine the precise extent to which the benefits of platooning can be expanded through the incorporation of exchange-based incentives. We focus on two mechanisms for incentivized platooning: (i) incentivized “catch-up” along a prescribed highway route and (ii) incentivized re-routing to allow for platooning. For the incentivized “catch-up” mechanism, platoon capable vehicles begin at staggered positions, using a novel platoon catch-up algorithm capable of determining the fuel-optimal platoon engagement position and fuel-optimal velocity trajectories. Additionally, the incentivized re-routing mechanism determines the optimal route for a network of platoon-capable vehicles, allowing for a vehicle to reroute its trajectory to engage within the platoon. Because such scenarios will be shown to frequently lead to aggregate benefit, while actually hurting the fuel economy of one or more participants, we propose three methods for explicitly computing the monetary value of the exchange. Assuming a known trajectory and traffic pattern, the first uses the Shapley value to determine the exchange value. The second method adjusts the Shapley value, accounting for uncertainty associated with traffic modeling. The final method assumes a competitive market, requiring each individual operator to implement a bid.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Earnhardt, Christian and Groelke, Ben and Borek, John and Pelletier, Evan and Brennan, Sean and Vermillion, Chris}, year={2022}, month={May} }
 @article{groelke_earnhardt_borek_vermillion_2021, title={A Predictive Command Governor-Based Adaptive Cruise Controller With Collision Avoidance for Non-Connected Vehicle Following}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2021.3112113}, abstractNote={This paper presents a command governor (CG) based adaptive cruise controller (ACC) that is applied in simulation to normal driving scenarios and emergency stopping scenarios. The vehicle-following case study used in this paper involves a heavy-duty ego vehicle and a light-duty non-connected lead vehicle (i.e., the ego vehicle does not communicate with the lead vehicle and can only infer the lead vehicles’ position and velocity states through its own sensors). Typically, to ensure constraints in the presence of disturbances, receding horizon based ACCs will assume some known worst-case behavior of the lead vehicle. In the presence of a stochastic, non-connected lead vehicle, however, achieving such a guarantee requires a worst-case assumption on the behavior of the lead vehicle for all future time. In this work, the CG assumes a lead vehicle velocity profile that will be achieved with a prescribed level of certainty, based on a stochastic characterization of lead vehicle behavior that has been informed by actual on-road data. The CG ensures safe following distance under this probabilistic lead vehicle assumption. Here, “safe following distance” is based on the ego vehicle’s ability to come to a stop without collision if the lead vehicle were to suddenly brake at maximum deceleration after proceeding at a velocity profile that is prescribed based on a statistical lower bound on lead vehicle velocity. Ultimately, the CG ensures that the worst-case safe following distance is satisfied with a prescribed probability, thereby paralleling chance-constrained CG formulations. Simulation results for a heavy-duty truck indicate that the CG-based ACC outperforms a PID-ACC in terms of fuel economy and drivability. Additionally, the CG-ACC approach was able to ensure rear-end collision avoidance in emergency stopping simulations.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Groelke, Ben and Earnhardt, Christian and Borek, John and Vermillion, Chris}, year={2021}, month={Oct} }
 @article{groelke_borek_earnhardt_vermillion_2021, title={Design and Performance Analysis of a Cascaded Model Predictive Controller and Command Governor for Fuel-Efficient Control of Heavy-Duty Trucks}, volume={143}, ISSN={["1528-9028"]}, DOI={10.1115/1.4049544}, abstractNote={Abstract
               This paper presents the design and analysis of a predictive ecological control strategy for a heavy-duty truck that achieves substantial fuel savings while maintaining safe following distances in the presence of traffic. The hallmark of the proposed algorithm is the fusion of a long-horizon economic model predictive controller (MPC) for ecological driving with a command governor (CG) for safe vehicle following. The performance of the proposed control strategy was evaluated in simulation using a proprietary medium-fidelity Simulink model of a heavy-duty truck. Results show that the strategy yields substantial fuel economy improvements over a baseline, the extent of which are heavily dependent on the horizon length of the CG. The best fuel and vehicle-following performance are achieved when the CG horizon has a length of 20–40 s, reducing fuel consumption by 4–6% when compared to a Gipps car-following model.}, number={6}, journal={Journal of Dynamic Systems, Measurement, and Control}, author={Groelke, Ben and Borek, John and Earnhardt, Christian and Vermillion, Chris}, year={2021}, month={Jun}, pages={061009} }
 @article{borek_goelke_earnhardt_vermillion_2020, title={Hierarchical Control of Heavy-Duty Trucks Through Signalized Intersections With Non-Deterministic Signal Timing}, volume={23}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2021.3128068}, abstractNote={This paper presents a hierarchical Green-Light Approach Speed (h-GLAS) strategy for controlling heavy-duty trucks traveling through urban/suburban environments, where future intersection signal phase and timing (SPaT) information is non-deterministic. Through vehicle-to-infrastructure communication, past SPaT information is sent to the vehicle and is used to forecast future predictions of the signal timing using a Gaussian Process (GP) model. The h-GLAS strategy uses the predicted SPaT information to generate an efficient desired velocity profile that navigates through intersections where the probability of a green light is maximized. This velocity profile is tracked by a convex model predictive controller (MPC) that simultaneously minimizes mechanical energy expenditure and braking effort over its prediction horizon. Downstream from the MPC, we implement a command governor (CG) that adjusts the MPC output by the minimum amount necessary to maintain safe vehicle following and ensure that the vehicle stops at all red lights. Using a proprietary medium-fidelity Simulink model provided by Volvo Trucks, we characterize the h-GLAS strategy’s performance over a real suburban route, consisting of 10 actuated signalized intersections, using actual SPaT information provided by the NC Department of Traffic (DOT). Simulation results demonstrate a 30-43% reduction in fuel consumption, as compared to a baseline control strategy, which is attributable primarily to avoiding the massive energy losses associated with braking. Furthermore, we evaluate the computational efficiency of our approach by assessing average simulation execution times.}, number={8}, journal={IEEE Transactions on Intelligent Transportation Systems}, author={Borek, John and Goelke, Ben and Earnhardt, Christian and Vermillion, Chris}, year={2020}, month={Sep}, pages={13769–13781} }
 @article{earnhardt_groelke_borek_vermillion_2021, title={Hierarchical Model Predictive Control Approaches for Strategic Platoon Engagement of Heavy-Duty Trucks}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2021.3076963}, abstractNote={For a group of vehicles, collaborative platooning can be valuable in certain situations due to aerodynamic drag reduction, while being detrimental or altogether impractical in others. This paper details a platoon engagement/disengagement controller, which alternates between velocity trajectory optimization (VTO) in isolation and a fused platooning and VTO approach, capable of disengaging a platoon during segments detrimental to fuel savings and rejoining the platoon afterwards without significant energy expenditure. The proposed approach leverages parallel model predictive control (MPC) computations that (i) can identify when a platoon should be engaged/disengaged and (ii) performs the engagement/disengagement in a fuel-optimal manner. Using a medium-fidelity Simulink model furnished by Volvo, two real-world trucking routes, and two different traffic scenarios, the effectiveness of the approach was compared against non-platooning VTO, as well as a baseline controller that uses a PI-based cruise controller that incorporates Gipps car-following model. Results for a two-vehicle platoon using a 1 vehicle following distance reveal a 9.6% to 11.9% decrease in aggregate fuel consumption for both vehicles within the platoon, as compared to the baseline, highlighting the ability to disengage and rejoin a platoon without expending unnecessary fuel consumption. Additionally, the approaches for disengaging a platoon result in a 4-7% decrease in aggregate fuel consumption, as compared to a VTO-only approach.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Earnhardt, Christian and Groelke, Ben and Borek, John and Vermillion, Chris}, year={2021}, month={May} }
 @article{earnhardt_groelke_borek_naghnaeian_vermillion_2021, title={A Multirate, Multiscale Economic Model Predictive Control Approach for Velocity Trajectory Optimization of a Heavy Duty Truck}, volume={143}, ISSN={["1528-9028"]}, DOI={10.1115/1.4048658}, abstractNote={Abstract
               This paper introduces a hierarchical economic model predictive control (MPC) approach for maximizing the fuel economy of a heavy-duty truck, which simultaneously accounts for aggregate terrain changes that occur over very long length scales, fine terrain changes that occur over shorter length scales, and lead vehicle behavior that can vary over much shorter time/length scales. To accommodate such disparate time and length scales, the proposed approach uses a multilayer MPC approach wherein the upper-level MPC uses a long distance step, a long time-step, and coarse discretization to account for the slower changes in road grade, while the lower-level MPC uses a shorter time-step to account for fine variations in road grade and rapidly changing lead vehicle behavior. The benefit of this multirate, multiscale approach is that the lower-level MPC leverages the upper-level's sufficiently long look-ahead while allowing for safe vehicle following and adjustment to fine road grade variations. The proposed strategy has been evaluated over four real-world road profiles in both open-highway and traffic environments, using a medium-fidelity simulink model furnished by Volvo Group North America. Compared with a conventional cruise control system plus vehicle following controller as a baseline, results show 4–5% fuel savings in an open highway setting and 6–8% fuel savings in the presence of traffic, without compromising trip time.}, number={3}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Earnhardt, Christian and Groelke, Ben and Borek, John and Naghnaeian, Mohammad and Vermillion, Chris}, year={2021}, month={Mar} }
 @article{borek_groelke_earnhardt_vermillion_2020, title={Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment}, volume={28}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2019.2918472}, abstractNote={This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, off-line dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. The three candidate economic MPC formulations that are evaluated include a nonlinear time-based formulation that directly penalizes the predicted fuel consumption, a nonlinear time-based formulation that penalizes the braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck’s longitudinal and engine dynamics, we analyze the optimization’s performance on four highway routes under various traffic scenarios. Results demonstrate 3.7%–8.3% fuel economy improvement on highway routes without traffic and 6.5%–10% on the same routes with traffic included. Furthermore, we present a detailed analysis of energy usage by “type” (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control strategy.}, number={5}, journal={IEEE Transactions on Control Systems Technology}, author={Borek, John and Groelke, Ben and Earnhardt, Christian and Vermillion, Chris}, year={2020}, month={Sep}, pages={1652–1664} }