Collaborative Platooning of Automated Vehicles Using Variable Time-Gaps

Abstract

Connected automated vehicles (CAVs) could potentially be coordinated to safely attain the maximum traffic flow on roadways under dynamic traffic patterns, such as those engendered by the merger of two strings of vehicles due a lane drop. Strings of vehicles have to be shaped correctly in terms of the inter-vehicular time-gap and velocity to ensure that such operation is feasible. However, controllers that can achieve such traffic shaping over the multiple dimensions of target time-gap and velocity over a region of space are unknown. The objective of this work is to design such a controller, and to show that we can design candidate time-gap and velocity profiles such that it can stabilize the string of vehicles in attaining the target profiles. Our analysis is based on studying the system in the spacial rather than the time domain, which enables us to study stability as in terms of minimizing errors from the target profile and across vehicles as a function of location. Finally, we conduct numeral simulations in the context of shaping two platoons for merger, which we use to illustrate how to select time-gap and velocity profiles for maximizing flow and maintaining safety.

Spring–Mass–Damper-Based Platooning Logic for Automated Vehicles

“…HasanzadeZonuzy et al ( 20 ) designed a CACC logic with space-based model parameters to improve the merging maneuvers of CACC platoons. The idea was to assign each vehicle on the sub-stream to a pair of vehicles on the mainstream, then adjust the time gap and velocity of vehicles on the mainstream to accommodate the merging vehicle.…”

A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

“…where 10 01 C  =   (23) Eq. 22describes the measurement (observation) equality, in which () yt is the measurement detecting from AV's on-board sensor and () Wt represents the sensor error (disturbance) at time t .…”

“…As far as authors know, current ACC control algorithms cannot really incorporate users driving preference [21]. Besides, as AV's market penetration grows gradually, the number of user-preferred settings can naturally diversify [22], [23]. Among all the personal settings, risk-taking preference, also known as risk-sensitivity, is an extremely important characteristic due to the fact that it has a close relationship with the safety of car-following behaviors and should be heterogeneous for different drivers depending on the states and current driving situations.…”

“…Additionally, comfort in AV control process is also critical. To avoid sudden starts and stops, expensive control strategy [23] which has an extra penalty on large control input (acceleration) can also help to smooth car-following behaviors based on different drivers' preference. Hence, to increase the diversity of ACC controller behaviors, we present a personalized stochastic optimal control algorithm for AV.…”

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

“…This study proposed a unified multiclass model for a heterogeneous platoon that analyses the system steady-state errors and transient-state performance. Furthermore, a controller was developed to obtain string stability in vehicle platoon using feedback linearization and further extended to attain merging of a two string of vehicles using time-gap and velocity profiles [53]. Here, input to the controller was given by time-gap and velocity profiles and string stability analysis was studied for both velocity tracking errors and time-gap tracking errors.…”