Publication

The effect of the interaction between Cooperative, Connected and Automated Mobility (CCAM) in scenarios where the ratio of conventional and intelligent vehicles is a topic of interest for researchers and the industry. There is an expected effect on road safety and traffic efficiency stemming from the coexistence between Connected Automated Vehicles (CAVs) and other non-connected, non-automated road users like disconnected pedestrians or legacy vehicles – which we group under the term Multi-modal Road Users (MRUs). These effects, either positive or negative, might not grow linearly with increased adoption rates and, furthermore, it is realistic to expect MRUs to share the road with CAVs even as outliers in the fleet. Thus, identifying and analyzing if existing and developing safety metrics apply to a near full-CCAM environment is crucial, specifically, if human factors (which affect conventional driving) continue to influence safety and efficiency when full connection and automation is expected.
This pre-study explores the literature and performs a simulation-based analysis of these risks and human factors on road safety and traffic efficiency. Starting from an exploration of the literature, where groundwork exists on existing and expected risks and mitigations, we map these risks to scenarios where full CAV driving is expected. We identify that some of the humanly influenced risk factors that affect the Dynamic Driving task (DDT) are mitigated, while some other (e.g., decision making at the design stage of CAVs) might even create more heterogeneity. Then, in the simulation stage, we identify that heterogeneity is what influences safety and efficiency, and that it is not only the ratio between CAVs and MRUs that affects convergence but also the place in which heterogeneous vehicles are placed in the flow.