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Title: Experimental testing of a control barrier function on an automated vehicle in live multi-lane traffic

Conference · · 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS)
 [1];  [1];  [2];  [2];  [1]
  1. Vanderbilt University,Institute for Software Integrated Systems,Department of Civil and Environmental Engineering,Nashville,TN
  2. Vanderbilt University,Institute for Software Integrated Systems,Department of Computer Science,Nashville,TN
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This paper experimentally tests an implementation of a control barrier function (CBF) designed to guarantee a minimum time-gap in car following on an automated vehicle (AV) in live traffic, with a majority occurring on freeways. The CBF supervises a nominal unsafe PID controller on the AV’s velocity. The experimental testing spans two months of driving, of which 1.9 hours of data is collected in which the CBF and nominal controller are active. We find that violations of the guaranteed minimum time-gap are observed, as measured by the vehicle’s on-board radar unit. There are two distinct causes of the violations. First, in multi-lane traffic, Cut-ins from other vehicles represent external disturbances that can immediately violate the minimum guaranteed time gap provided by the CBF. When cut-ins occur, the CBF does eventually return the vehicle to a safe time gap. Second, even when cut-ins do not occur, system model inaccuracies (e.g., sensor error and delay, actuator error and delay) can lead to violations of the minimum time-gap. These violations are small relative to the violations that would have occurred using only the unsafe nominal control law.

Research Organization:
Vanderbilt Univ., Nashville, TN (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office
Contributing Organization:
Additional support is provided by the DOT Eisenhower Fellowship, and NSF awards 2135579 and 1837652. George Gunter is supported by an NSF Graduate Fellowship.
DOE Contract Number:
EE0008872
OSTI ID:
1874472
Journal Information:
2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Conference: The 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities (DI-CPS), remote, May 3, 2022
Country of Publication:
United States
Language:
English

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