Dinar, Y.; Qurashi, M.; Papantoniou, P.; Antoniou, C. How Do Humanlike Behaviors of Connected Autonomous Vehicles Affect Traffic Conditions in Mixed Traffic? Sustainability 2024, 16, 2402. https://doi.org/10.3390/su16062402
Dinar, Y.; Qurashi, M.; Papantoniou, P.; Antoniou, C. How Do Humanlike Behaviors of Connected Autonomous Vehicles Affect Traffic Conditions in Mixed Traffic? Sustainability 2024, 16, 2402. https://doi.org/10.3390/su16062402
Dinar, Y.; Qurashi, M.; Papantoniou, P.; Antoniou, C. How Do Humanlike Behaviors of Connected Autonomous Vehicles Affect Traffic Conditions in Mixed Traffic? Sustainability 2024, 16, 2402. https://doi.org/10.3390/su16062402
Dinar, Y.; Qurashi, M.; Papantoniou, P.; Antoniou, C. How Do Humanlike Behaviors of Connected Autonomous Vehicles Affect Traffic Conditions in Mixed Traffic? Sustainability 2024, 16, 2402. https://doi.org/10.3390/su16062402
Abstract
Consistent increment of road traffic in cities is remained a threat to traffic performance and causes confliction. The researcher repeatedly emphasized the connected autonomous vehicles (CAVs) as stronger solution to deal with traffic related issues. While having autonomous system in the dedicated lanes is found to be safe by many investigators because of predictable surrounding maneuvers, in contrast, the mixed lane states remain questionable. Different methodologies are being used to study the effects of autonomous vehicles (AVs) in the mixed traffic. Microscopic simulation tools are popular in such cases as it offers scope to experiment in cheap, robust, and optimistic way. One interesting methodology to deal with CAVs is considering it as conventional human driven vehicles and predict its possible characteristics based on the simulation inputs. One big challenge is, as there is no regular real-world data to calibrate and to validate the simulated model for CAVs. This is where conventional human driven vehicles from real world, come to aid as benchmark to offer the measure of effectiveness (MoE) for the calibration and validation. For the three most common driving modules, a sensitivity analysis of the driving behaviors of autonomous vehicles (AVs) and an effect assessment of CAVs in a mixed traffic environment were done to explore the human alike autonomous technology. The findings show that, up to a point, which is directly related to the quantity of interacting vehicles, the impact of CAVs is typically favorable. This study supports earlier research by demonstrating that CAVs outperform AVs in traffic. In addition, the sensitivity analysis has shown that technical and infrastructural advances are necessary to achieve the greatest benefits.
Keywords
Autonomous vehicle; CAVs; Sensitivity Analysis
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
