Source 1primary paper2010IEEE Transactions on Control Systems Technology
Toolkit/Optimization-based control algorithm
Optimization-based control algorithm
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
The optimization-based control algorithm is an engineering method for predictive cruise control that uses short-range radar and traffic signal information to schedule an optimum vehicle velocity trajectory. It is intended to guide vehicle motion relative to upcoming signal phases and nearby traffic.
Usefulness & Problems
Why this is useful
This method is useful for coordinating vehicle speed with traffic signals and surrounding vehicles using predictive information. Source literature indicates that it targets improved fuel economy, reduced emissions, and reduced trip time in simulation case studies.
Problem solved
It addresses the control problem of planning a vehicle velocity trajectory that supports timely arrival at a green light with minimal braking while maintaining safe inter-vehicle distance and cruising at or near a set speed. The method specifically uses predictive traffic signal and short-range radar inputs to inform that trajectory scheduling.
Problem links
Need precise spatiotemporal control with light input
DerivedThe optimization-based control algorithm is an engineering method for predictive cruise control that uses short-range radar and traffic signal information to schedule an optimum vehicle velocity trajectory. It is designed to guide vehicle motion relative to upcoming signal phases and surrounding traffic.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete method used to build, optimize, or evolve an engineered system.
Techniques
No technique tags yet.
Target processes
No target processes tagged yet.
Input: Light
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationimplementation constraint: spectral hardware requirementoperating role: builder
Implementation requires short-range radar and traffic signal information as predictive inputs for scheduling the vehicle velocity trajectory. The available evidence does not specify software architecture, controller parameterization, sensing interfaces, or real-time deployment constraints.
The supplied evidence describes simulation case studies but does not report experimental road validation, hardware deployment, or independent replication. The evidence also does not provide algorithmic details such as optimization formulation, computational requirements, or robustness under sensing uncertainty.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
simulation case studies 3
Approval Evidence
1 source3 linked approval claimsfirst-pass slug optimization-based-control-algorithm
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
Source:
control objectivesupports
The control objectives are timely arrival at a green light with minimal braking, maintaining safe distance between vehicles, and cruising at or near set speed.
The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed.
Source:
method descriptionsupports
The formulated optimization-based control algorithm uses short range radar and traffic signal information predictively to schedule an optimum vehicle velocity trajectory.
an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle
Source:
simulation result summarysupports
Three simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time.
Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
Source:
Comparisons
Source-backed strengths
The reported strengths are its explicit multi-objective formulation, including green-light arrival timing, minimal braking, safe following distance, and near-set-speed cruising. The cited study also reports three simulation case studies demonstrating potential impact on fuel economy, emission levels, and trip time.
Optimization-based control algorithm and Method for efficient synthesis of phycocyanobilin in cultured mammalian cells address a similar problem space.
Shared frame: same top-level item type; same primary input modality: light
Strengths here: may avoid an exogenous cofactor requirement.
Compared with optogenetic
Optimization-based control algorithm and optogenetic address a similar problem space.
Shared frame: same top-level item type; same primary input modality: light
Compared with reversible optogenetic unmasking-masking of Ct residues
Optimization-based control algorithm and reversible optogenetic unmasking-masking of Ct residues address a similar problem space.
Shared frame: same top-level item type; same primary input modality: light
Ranked Citations
- 1.