An Improved Lane-changing Model for Connected Automated Vehicles Under Cyberattacks

Abstract:

The connected automated vehicle （CAV） is promising to enhance traffic efficiency， traveling safety， and energy savings. However， due to the open wireless communication， the CAV is vulnerable to cyber threats. Existing studies mainly focus on surveying related cyberattacks and evaluating the impact of attacks on vehicular longitudinal behaviors on a single lane. This paper aims to investigate the effects of cyberattacks on vehicular lateral behaviors on a two-lane highway， i.e.， the lane-changing （LC） behaviors under cyberattacks. Based on a classical lane-changing model--minimizing overall braking induced by lane changes （MOBIL） model， and a classical car-following model--Intelligent Driver Model （IDM）， this study proposes an extended lane-changing model （ELC） which can model CAV's lane-changing behaviors under cyberattacks. At the end， simulations are conducted to illustrate the impact of different malicious attacks on vehicles' LC movements. Results show that cyberattacks can imperil the LC maneuvers and lead to abnormal driving behaviors.