A Comparative Study of Longitudinal Vehicle Control Systems in
Vehicle-to-Infrastructure Connected Corridor 12-06-04-0025
This also appears in
SAE International Journal of Connected and Automated Vehicles-V132-12EJ
Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity
for vehicles to perform autonomous longitudinal control to navigate safely and
efficiently through sequences of V2I-enabled intersections, known as connected
corridors. Existing research has proposed several control systems to navigate
these corridors while minimizing energy consumption and travel time. This
article analyzes and compares the simulated performance of three different
autonomous navigation systems in connected corridors: a V2I-informed constant
acceleration kinematic controller (V2I-K), a V2I-informed model predictive
controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent.
A rules-based controller that does not use V2I information is implemented to
simulate a human driver and is used as a baseline. The performance metrics
analyzed are net energy consumption, travel time, and root-mean-square (RMS)
acceleration. Two connected corridor scenarios are created to evaluate these
metrics, including one scenario reconstructed from real-world traffic signal
data. A sensitivity analysis is also performed to quantitatively identify key
parameters that have the highest impact on the three metrics of interest.
Citation: King, B., Olson, J., Hamilton, K., Fitzpatrick, B. et al., "A Comparative Study of Longitudinal Vehicle Control Systems in Vehicle-to-Infrastructure Connected Corridor," SAE Intl. J CAV 6(4):397-413, 2023, https://doi.org/10.4271/12-06-04-0025. Download Citation
Author(s):
Brian King, Jordan Olson, Kayla Hamilton, Benjamin Fitzpatrick, Hwan-Sik Yoon, Paul Puzinauskas
Affiliated:
The University of Alabama, Mechanical Engineering, USA, The University of Alabama, Electrical and Computer Engineering,
USA
Pages: 18
ISSN:
2574-0741
e-ISSN:
2574-075X
Related Topics:
Control systems
Navigation and guidance systems
Vehicle to infrastructure (V2I)
Energy consumption
Machine learning
Autonomous vehicles
Traffic management
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