Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks' energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render ...lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin's minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user's expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%-13.7% reduction in energy consumption and a 5.2%-10.3% reduction in the travel time, along with navigational improvements.
Connected intelligent vehicle following can improve safety and efficiency compared to today's road transport, but real traffic in the near future will not provide an ideal setting for its deployment. ...Unconnected human-driven vehicles will follow variable behavior patterns that combine with differences in dynamic capability to create heterogeneous scenarios. In this paper, connected automated vehicles employ model predictive control for following in traffic that may include both heavy and passenger vehicles at quasi-random positions. Adding further realism, some quasi-randomly mixed vehicles are completely unconnected and equipped with the reactive intelligent driver model using pseudorandom parameters. A mixed-integer quadratic programming formulation adapts the predictive algorithm to diverse powertrain operating point constraints. The preceding vehicle's control input is estimated from velocity and brake light observations and used to probabilistically generate a preview for the ego vehicle. A terminal constraint designed using particle kinematics prevents collisions due to shortsightedness. Results are simulated at various heavy and predictive vehicle concentrations in the presence of packet loss. Linear fuel economy improvements between 1.4% and 1.9% per 10% increase in predictive vehicle penetration rate are shown.
Emissions of nitrogen oxides from road vehicles pose a public health hazard because of their role in smog and acid rain formation. Although a great body of research exists on driving techniques to ...reduce vehicular fuel use and carbon dioxide emissions, solutions for other pollutants such as nitrogen oxides are not nearly as well studied. Unfortunately, nitrogen oxides do not necessarily trend with fuel consumption as carbon dioxide emissions do and the fuel-minimal solution may produce excess nitrogen oxides. This article addresses the emissions eco-driving problem for compression-ignition engines with EGR and SCR using an optimal control approach based on Pontryagin's minimum principle (PMP). In anticipation of eco-coaching applications, a simplified piecewise-affine model results in optimal acceleration as rational functions of speed. The effectiveness of this approach is compared to dynamic programming (DP) and heuristic rules from an available eco-driving mobile app. When applied to a real-world human driving dataset from an urban area, the proposed technique reduced modeled emissions of nitrogen oxides by 35%-36% while simultaneously reducing carbon dioxide emissions.
Connected and automated vehicles (CAVs) have shown great potential in improving the energy efficiency of road transportation. Energy savings, however, greatly depends on driving behavior. Therefore, ...the controllers of CAVs must be carefully designed to fully leverage the benefits of connectivity and automation, especially if CAVs travel amongst other non-connected and human-driven vehicles. With this as motivation, we introduce a framework for the longitudinal control of CAVs traveling in mixed traffic including connected and non-connected human-driven vehicles. Reactive and predictive connected cruise control strategies are proposed. Reactive controllers are given by explicit feedback control laws. Predictive controllers, on the other hand, optimize the control input in a receding-horizon fashion, by predicting the motions of preceding vehicles. Beyond-line-of-sight information obtained via vehicle-to-vehicle (V2V) communication is leveraged by the proposed reactive and predictive controllers. Simulations utilizing real traffic data show that connectivity can bring up to <inline-formula><tex-math notation="LaTeX">30\%</tex-math></inline-formula> energy savings in certain scenarios.
This paper studies the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected ...mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%–20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%–9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.
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•Considers conventional, electric, and hybrid vehicle types for energy effects.•Introduce probabilistic constraints for safety and traffic flow considerations.•Integrates empirical data and high fidelity models for increased realism in simulation.•Mixed fleets observe improved energy and flow effects.
•Embeds physical vehicles into a virtual traffic scene for energy and flow evaluation.•Measures conventional and electric powertrain types for energy effects.•Introduces probabilistic constraints to ...balance safety and traffic flow considerations.•Fuses data-driven techniques and classical techniques for automated vehicle control.
This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), without sacrificing safety and traffic flow. We implement a Vehicle-in-the-Loop (VIL) testing environment in which experimental CAVs driven on a track interact with surrounding virtual traffic in real-time. We explore the energy savings when following city and highway drive cycles, as well as in emergent highway traffic created from microsimulations. Model predictive control handles high level velocity planning and benefits from communicated intentions of a preceding CAV or estimated probable motion of a preceding human driven vehicle. A combination of classical feedback control and data-driven nonlinear feedforward control of pedals achieve acceleration tracking at the low level. The controllers are implemented in ROS and energy is measured via calibrated OBD-II readings. We report up to 30% improved energy economy compared to realistically calibrated human driver car-following without sacrificing following headway.
In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the ...unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.
This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), ...without sacrificing safety and traffic flow. We implement a Vehicle-in-the-Loop (VIL) testing environment in which experimental CAVs driven on a track interact with surrounding virtual traffic in real-time. We explore the energy savings when following city and highway drive cycles, as well as in emergent highway traffic created from microsimulations. Model predictive control handles high level velocity planning and benefits from communicated intentions of a preceding CAV or estimated probable motion of a preceding human driven vehicle. A combination of classical feedback control and data-driven nonlinear feedforward control of pedals achieve acceleration tracking at the low level. The controllers are implemented in ROS and energy is measured via calibrated OBD-II readings. Here, we report up to 30% improved energy economy compared to realistically calibrated human driver car-following without sacrificing following headway.
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