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Automated Vehicles in Hazardous Merging Traffic: A Chance-Constrained Approach

Conference · · IFAC-PapersOnLine
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To realize the energy efficiency and productivity benefits of automated driving, control algorithms must function safely among conventional vehicles. Prototype tests on public roads have revealed a trend of human-driven vehicles rear-ending automated ones. A popular belief holds that unusually conservative control algorithms play a role in such collisions. In August 2018, an automated vehicle was rear-ended while waiting to merge. Inspired by that incident, this paper examines a resemblant scenario in simulation using model predictive control for the automated vehicle. Constraint setup alternatives to avoid collisions with inattentive following vehicles are proposed and assessed in this simulation environment. In one variant, imminent rear-end collisions are detected and constraints are modified to promote more aggressive merging during such an event. Results show that higher-performing chance constraint designs can reduce collision probability, but may have other adverse effects depending on the particular algorithm.

Research Organization:
Clemson Univ., SC (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office
DOE Contract Number:
EE0008232
OSTI ID:
1863224
Journal Information:
IFAC-PapersOnLine, Vol. 52, Issue 5; Conference: 9th IFAC Symposium on Advances in Automotive Control AAC 2019 Orléans, France, 23–27 June 2019; ISSN 2405-8963
Country of Publication:
United States
Language:
English

References (8)

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Predictively Coordinated Vehicle Acceleration and Lane Selection Using Mixed Integer Programming
  • No authors listed
  • ASME 2018 Dynamic Systems and Control Conference, Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare https://doi.org/10.1115/DSCC2018-9177
November 2018
Efficient control of fully automated connected vehicles at freeway merge segments July 2017
Calibration and evaluation of car following models using real-world driving data October 2017
A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps May 2017
Congested traffic states in empirical observations and microscopic simulations August 2000
Probabilistic Anticipation and Control in Autonomous Car Following January 2019
Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge October 2017

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