Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints

Abstract

Emergent cooperative adaptive cruise control (CACC) strategies being proposed in the literature for platoon formation in the Connected Autonomous Vehicle (CAV) context mostly assume idealized fixed information flow topologies (IFTs) for the platoon, implying guaranteed vehicle-to-vehicle (V2V) communications for the IFT assumed. In reality, V2V communications are unreliable due to failures resulting from communication-related constraints such as interference and information congestion. Since CACC strategies entail continuous information broadcasting, communication failures can occur in congested CAV traffic networks, leading to a platoon's IFT varying dynamically. To explicitly factor IFT dynamics and to leverage it to enhance the performance of CACC strategies, this study proposes the idea of dynamically optimizing the IFT for CACC, labeled the CACC-OIFT strategy. Under CACC-OIFT, the vehicles in the platoon cooperatively determine in real-time which vehicles will dynamically deactivate or activate the "send" functionality of their V2V communication devices to generate IFTs that optimize the platoon performance in terms of string stability under the ambient traffic conditions. The CACC-OIFT consists of an IFT optimization model and an adaptive Proportional-Derivative (PD) controller. Given the adaptive PD controller with a two-predecessor-following scheme, and the ambient traffic conditions and the platoon size just before the start of a time period, the IFT optimization model determines the optimal IFT (in terms of the activated and deactivated status of the "send" functionalities of the vehicles in the platoon) that maximizes the expected string stability. This expectation is because each IFT has specific degeneration scenarios whose probabilities are determined by the communication failure probabilities for that time period based on the ambient traffic conditions. The optimal IFT is deployed for that time period, and the adaptive PD controller continuously determines the car-following behaviors of the vehicles based on the unfolding degeneration scenario for each time instant within that period. The effectiveness of the proposed CACC-OIFT is validated through numerical experiments in NS-3 based on NGSIM field data. The results indicate that the proposed CACC-OIFT can significantly enhance the string stability of platoon control in an unreliable V2V communication context, outperforming CACCs with fixed IFTs or with passive adaptive schemes for IFT dynamics.

DDPG-Based Decision-Making Strategy of Adaptive Cruising for Heavy Vehicles Considering Stability

“…Among the 8 factors of MDSI, dissociative driving factor has 8 items (11,15,27,30,34,35,36,39), anxious driving factor has 7 items (4,7,10,25,31,33,40), risky driving factor has 5 items (6,20,22,24,44), angry driving factor has 5 items (3,12,19,28,43), high-velocity driving factor has 6 items (2,5,9,16,17,32), distress-reduction driving factor has 4 items (1,8,26,37), patient driving factor has 4 items (13,18,23,38), careful driving factor has 5 items (14,21,29,41,42), 4 of them are reversed items.…”

“…7(a). The vertical load change of the first axis lateral moment transformation F zrm1,zlm1 , and vertical load change of the pitch moment transformation F zra1,zla1 , which is the virtual internal force applied to the split part, are shown in (10).…”

“…The difficulty of the whole process lies in the accurate description of the formation and disengagement of CACC vehicle strings when CACC vehicles are mixed with human-driven vehicles in the traffic stream [9]. In addition, V2V communications are sometimes unreliable due to failures resulting from communication-related constraints, such as interference and information congestion [10], [11]. This may lead to sudden degradation of control of such vehicles from CACC to ACC mode, which may negatively influence traffic safety [12].…”

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

“…We thank Hao Peng and Kantapon Kaewtip for insightful discussions. The idea of this work originally came out during discussions of [28] and [29].…”

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

“…It is appealing because of the enhancement of vehicle performance and situational awareness. However, cooperative ACC can be unreliable due to the immaturity of vehicle to vehicle communication [7]. Hence, ACC is still worth investigating.…”