Empirical study on car-following characteristics of commercial automated vehicles with different headway settings*

A recent study (Li, 2020) analytically predicted tradeoffs between automated vehicle (AV) following characteristics on safety, mobility, and stability using a parsimonious linear car following model. This work aimed to verify the key theoretical findings in the above study with empirical experiments using commercial AVs, e.g., vehicles with adaptive cruise control (ACC) functions. We collect high-resolution trajectory data of multiple commercial AVs following one another in a platoon with different headway settings. Parsimonious linear AV-following models that capture the first-order parameters on safety, mobility, and stability aspects are estimated with the data. The estimation results of the key parameters validate several theoretical predictions predicted by Li (2020). Specifically, it was found that as the time lag setting increases, the corresponding safety buffer decreases, indicating that AV safety could be improved with less pursuit of AV mobility or, conversely, AV mobility improvement may come at a cost of more stringent safety requirements. Also, as the time lag setting increases, AV string stability increases, indicating that stop-and-go traffic potentially could be dampened by compromising AV mobility. With this, one possible explanation to the observed string instability of commercial AV following control (i.e., ACC function) is that automakers may prefer to ensure a relatively short headway (and thus better user experience on vehicle mobility) at a cost of compromising string stability. It was also found that as the time lag increases, the cycle period of traffic oscillations gets longer, and the oscillation amplification gets smaller, which supports the tradeoff between mobility and stability. On the other hand, field experiments revealed issues beyond the predictivity of a simple linear model. That is, vehicle control sensitivity factors vary across different speed and headway settings, and the model estimation results for key parameters are not consistent over different speed ranges. This opens future research needs for investigating nonlinearity and stochasticity in the AV following modeling.