A Platooning Strategy for Automated Vehicles in the Presence of Speed Limit Fluctuations

Abstract

Platooning is expected to enhance the efficiency of operating automated vehicles. The positive impacts of platooning on travel time reliability, congestion, emissions, and energy consumption have been shown for homogenous roadway segments. However, the transportation system consists of inhomogeneous segments, and understanding the full impacts of platooning requires investigation in a realistic setup. One of the main reasons for inhomogeneity is speed limit fluctuations. Speed limit changes frequently throughout the transportation network, due to safety-related considerations (e.g., changes in geometry and workzone operations) or congestion management schemes (e.g., speed harmonization systems). In the current transportation systems with human-driven vehicles, these speed drops can potentially result in shockwave formation, which can cause travel time unreliability. Automated vehicles, however, have the potential to prevent shockwave formation and propagation and, therefore, enhance travel time reliability. Accordingly, this study presents a constant time headway strategy for automated vehicle platooning to ensure accurate tracking of any velocity profile in the presence of speed limit fluctuations. The performance of the presented platooning strategy is compared with Gipps’ car-following model and intelligent driver model, as representatives of regular non-automated vehicles. Simulation results show that implementing a fully autonomous system prevents shockwave formation and propagation, and enhances travel time reliability by accurately tracking the desired velocity profile. Moreover, the performance of platoons of regular and automated vehicles is investigated in the presence of a speed drop. The results show that as the market penetration rate of automated vehicles increases, the platoon can track the velocity profile more accurately.

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

“…Wang et al developed a car-following model for CACC that incorporates the information from the leader to account for the behavior of more vehicles in the platoon ( 20 ). Arefizadeh et al and Arefizadeh and Talebpour have introduced a CACC system capable of preventing any shockwave formation when dealing with speed limit fluctuations ( 22 , 23 ). However, a key factor that most of the above studies did not capture is the interactions between human drivers and AVs.…”

On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles

“…Also, within a platoon, various scenarios are researched like in [68] authors target the catchup strategy (i.e., the upstream vehicles accelerate to catch up with the leading vehicles) and a slow-down strategy (i.e., the leading vehicle decelerate so that the upstream vehicle can catch up and platoon with them). [69] studies the behavior of platoon in the presence of speed limit fluctuations. Lastly, we mention work [70] where various communication protocols are compared for platooning.…”

A game-theoretic framework for autonomous vehicles velocity control: Bridging microscopic differential games and macroscopic mean field games

“…This paper primarily focuses on first-order MFGs and will leave the discussion on higher-order MFGs in future research. Assuming connectivity between predecessors and followers as well as between platoon leaders and followers, CACC contains two control policies: constant spacing (CS) [96,26,97] and constant time headway (CTH) [45,84,103,74,104,127,6,94]. These two policies can be formulated as a linear time invariant system (LTI) [98] with disturbances to dynamic and measurement dynamics [127] or a model predictive control (MPC) system with distributed control [107,34,33].…”

“…We will discretize the systems in space and time and then present a solution algorithm to compute the MFE. 6. MFE solution algorithm.…”

“…AV controls can be categorized into longitudinal control (i.e., the car-following scenario) and lateral control (i.e., the lane-change scenario). Longitudinal control has been studied in various scenarios, including: platooning [34,127,109,65], speed harmonization [70,71,6], longitudinal trajectory optimization [109,64], and ecoapproach and departure at signalized intersections [4,37,116]. Connected adaptive cruise control (CACC) is the most extensively studied longitudinal controller for AV platooning [96,26,45,84,103,74,104,52,90,74,80,73,72,99,47,109,64].…”