Traffic Engineering Considering Cooperative Vehicle Infrastructure System

Abstract

Transportation engineering focuses on the interaction between people, vehicles, and infrastructures to improve efficiency, safety, and environmental friendliness. Technologies associated with the cooperative vehicle infrastructure system (CVIS), are based on intelligent vehicles, vehicle-to-vehicle communication, and vehicle-to-infrastructure communication. This will eventually transform the transportation system configuration from human-vehicle-road coordination to vehicle-infrastructure cooperation, thereby significantly improving traffic efficiency and safety. In the CVIS environment, conventional human factors have a minimal impact and the intersection of people, vehicles, and roads is enhanced. Traffic systems can be controlled and evaluated effectively, and randomness can be reduced to a large extent. Perception, decision-making, and control are managed by robots instead of people, which could lead to numerous changes and challenges in fundamentals of traditional transportation engineering. In this paper, we will mainly discuss the influence of CVIS on transportation engineering, and the associated scientific issues.

Evolutionary Game and Simulation Analysis of Intelligent Connected Vehicle Industry With Cross-Border Collaborative Innovation

“…Google took the lead in initiating autonomous driving research and development (R&D) in the United States in 2009, and the technology has been gradually commercialized in recent years [4]. However, the disadvantages of high prices and restricted safety of SVI have increasingly emerged with the advent of autonomous vehicles in the automotive market [5]. According to an MIT assessment released in 2020, the SVIbased technology development route will take at least 10 years to attain large-scale industrialization.…”

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

“…With the rapid development of 5G communication and intelligent connected vehicles (ICVs), the trajectory prediction of ICVs under a vehicle-to-everything (V2X) [ 1 ] system has become an important technology to improve the service level of ICVs [ 2 , 3 , 4 ]. Meanwhile, considering that transportation requires high levels of efficiency and safety, the accuracy and lower latency performance of the trajectory prediction methods need to be further improved [ 5 , 6 , 7 ].…”

Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm

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