Optimal investment in driving automation: Individual vs. cooperative sensing

Abstract

Connected automated vehicles (CAVs) use sensors to scan their surrounding environment in order to make safe and efficient motion decisions. The richness of a CAV’s vision depends on the configuration of on-board sensors and the ability of CAVs to share data with nearby vehicles. High-end sensors provide quality data, allowing CAVs to rely on their own sensors through individual sensing, and to become independent of other vehicles. In contrast, sensor data sharing enhances the collective vision of CAVs through cooperative sensing. This study investigates the trade-offs in the values and costs of individual versus cooperative sensing, and it proposes optimal investment strategies for short- and long-term planning. Exploiting the setting of a CAV-exclusive corridor, we propose two nonlinear programs to determine optimal investment in corridor capacity in the long-term, and to maximize social welfare in the short-term through road pricing. Capacity analysis shows disjointed investments in individual and cooperative sensing, first in the former until sensors reach a desired resolution, and then in the latter. Social welfare analysis shows travelers are granted a reward in certain settings, encouraging flows that achieve cooperative sensing. Such rewards are not observed in human-driven vehicle settings that cannot benefit from cooperative sensing.