Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles

Abstract

As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights into the future use of autonomous vehicles by empirically examining the following distances achieved under different driving conditions. Controlled experiments were conducted using three vehicles equipped with various types of ACC sensors, and comparable scenarios were replicated with human drivers. The experiments involved driving at multiple constant speeds to evaluate the efficacy of ACC in maintaining safe following distances. Our findings indicate that ACC systems consistently converge on optimal following distances, demonstrating their ability to regulate spacing between vehicles effectively. However, a notable downside emerged in terms of their adverse impact on road capacities, where the results indicate a mitigation in capacity percentages of 7.6%, 9.3%, and 15.6% for the three types of ACC-equipped vehicles compared to human drivers. This study sheds light on the intricate interplay between ACC systems and human driving behaviors, emphasizing the need to consider both factors when envisioning the future of autonomous vehicles. While ACC systems provide a standardized and reliable approach to following distances, the shorter distances observed in human-driven scenarios suggest a potential trade-off between safety and traffic capacity. These insights contribute to a comprehensive understanding of the dynamics involved in autonomous driving, facilitating informed decision making for the integration of autonomous vehicles into future transportation systems.

GIS-Based Regional Seismic Risk Assessment for Dubai, UAE, Using NHERI SimCenter R2D Application

“…Additionally, these digital datasets can be used to train and verify artificial intelligence algorithms for urban planning, transportation, and infrastructural simulations, e.g., Refs. [32][33][34].…”

Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption

“…Based on the three methodology subsections, the last step includes calculating the overall fuel consumption for both human-driven vehicles and AVs the following equations [51][52][53]:…”

“…The experiments encompassed driving at various constant speeds to assess the effectiveness of ACC in upholding secure following distances. The results for the traffic capacities are derived from [53] and presented in Table 5. Nevertheless, only the capacity data obtained at constant speeds of 30, 40, and 50 km/h is utilized for the purpose of fitting the current research data.…”

“…Figures 18-25 present an overview of the total fuel consumption across the fifteen vehicles in three distinct scenarios: one involving HDV and two featuring AVs. To delve deeper into the ACC performance during the experiments in [53], AV1 relies on a visionbased camera sensor for the ACC in the car-following procedure. While AV2 utilizes a combined camera-radar sensor integrated into the ACC system.…”