Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

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 c...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2021-11, Vol.29 (6), p.2561-2574
Main Authors: Dollar, Robert Austin, Vahidi, Ardalan
Format: Article
Language:English
Subjects:
ADVANCED PROPULSION SYSTEMS
Algorithms
Automation
Control theory
Decentralized control
Driving conditions
Energy consumption
Horizon
Integer programming
Intelligent vehicles
Lane changing
Mathematical programming
Mixed integer
Modules
Motion planning
Nonlinear dynamics
Optimal control
Optimization
Reduction
Roads
Steering
Traffic
Travel time
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Summary: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.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2020.3046570