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

• Published in: Transportation research. Part C, Emerging technologies Emerging technologies, 2020-04, Vol.113, p.124-145
• Main Authors: Wang, Chaojie, Gong, Siyuan, Zhou, Anye, Li, Tao, Peeta, Srinivas
• Format: Article
• Language: English
• Subjects: CACC; Communication failure; Dynamic information flow topology; Optimization; String stability
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2019.04.010

•Propose the CACC-OIFT strategy to dynamically optimize information flow topology (IFT) for CACC.•Under CACC-OIFT, vehicles dynamically deactivate/activate “send” functionality of their V2V communication devices.•CACC-OIFT consists of an IFT optimization model and an adaptive Proportional- Derivative controller.•CACC-OIFT enhances string stability of platoon control in an unreliable V2V communication context. Emergent cooperative adaptive cruise control (CACC) strategies being proposed 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 that maximizes the expected string stability in terms of the energy of speed oscillations. 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 C