Urban Traffic Detection for Autonomous Vehicles
Abstract
Autonomous vehicles leverage advanced sensors, artificial intelligence, and automation, enabling self-navigation without human intervention. These vehicles hold the potential to significantly improve road safety, enhance efficiency, and revolutionize transportation systems by reshaping how vehicles perceive, interpret, and respond to their environment.
The demand for such vehicles arises from the desire for improved urban planning, decreased parking needs, and flexible public transportation. Automation reduces errors, optimizes traffic flow, and produces favorable economic results.
This study underscores the crucial importance of advanced traffic and lane detection in reinforcing the reliability and safety of autonomous vehicles, playing a pivotal role in their ongoing evolution.
The proposed system operates in real-time, employing dynamic traffic data to inform decision-making. It integrates inputs from cameras, processing parameters such as lane positions, obstacles, and traffic symbols. A centralized control system, comprising Raspberry Pi and Arduino as master-slave components, employs specialized models for lane, object, and traffic symbol detection. This architecture guarantees continuous real-time decision-making and optimizes resource allocation, promoting a resilient and adaptive autonomous driving paradigm. The comprehensive nature of this approach not only aligns with contemporary transportation requirements but also proactively tackles the challenges anticipated in the future urban mobility landscape.
References
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