Control barrier function supervision of an automated vehicle in live freeway traffic: Field experiments

This work presents testing of a Control Barrier Function (CBF) supervised Automated Vehicle (AV) in live freeway traffic. The CBF is designed and implemented using a common dynamical model for AV longitudinal control and a time-gap based safety property combined with CAN bus injection software/hardware. 1.17 hours of car-following data is collected from driving in congested freeway traffic. We analyze the extent to which the CBF controlled AV satisfied three properties: 1) forward-invariance of the safety property, 2) recovery of the safety property, and 3) collision avoidance. Our main findings are as follows. Forward-invariance was not strictly satisfied across all states. When trajectories begin satisfying the safety property it was violated by a maximum of \(3.4[m]\) , and \(90\%\) of violations were less than \(2[m]\) . Recovery was also not strictly satisfied across all states. For trajectories which begin outside of the safe set due to merge-in events, if the violation to the safety property was by more than \(5[m]\) the AV was recovering back to the safe set in more than \(98\%\) of the time. Finally, the minimum spacing-gap was \(11.7[m]\) . Across the tests the AV remained far from any collisions. We additionally analyze errors between control inputs and achieved outputs and hypothesize that unaccounted for modeling errors may lead to under-braking compared to what the CBF logic specifies, which may contribute to the observed property violations. Using the collected experimental data we consider the role that control input delay may have played as an unmodeled error source. We perform model calibration considering both a traditional goodness of fit metric as well as capacity to model observed safety violations. Finally we propose two new approaches for handling delay in CBF AV control both based on lead vehicle acceleration estimation and compare this to existing approaches. We use collected experimental data to quantify this. We find that our proposed approach requires less added robustness to achieve forward-invariance in safety compared to existing approaches.