TrustPH: Trustworthy Platoon Head Selection considering Cognitive Biases to enhance Secure Platooning in Intelligent and Connected Vehicles

Vehicle platooning is a well-known technique utilized in intelligent and connected vehicles, where a platoon head is responsible to safely lead and guide a group of user vehicles. Platoon head selection can be optimized for various objectives, such as efficiency, safety, and security. This work considers a reputation system based approach to enhance the trustworthiness of platoon head selection while maintaining existing security requirements. The selection of a platoon head is based on the analysis of historical data, collected during previous trips among all participating user vehicles. To enhance trustworthiness, we propose the TrustPH framework, in which cognitive biases in the feedback scores, provided by user vehicles, are examined and the scores are adjusted accordingly. To evaluate TrustPH, a number of datasets are generated including different scenarios of cognitive bias and malicious behavior in user vehicle scoring. The obtained results demonstrate the effectiveness of TrustPH in ignoring cognitive biases in the platoon head selection process while ensuring that certain types of malicious scores are removed.