AI-Based Systems for Autonomous Vehicle Nighttime Safety and Navigation
Keywords:
COVID-19 pandemic, LID-LAR instrumentsAbstract
Autonomous vehicles are expected as a way to reduce traffic-related fatalities. Nighttime traffic causes only about one-third of fatalities but more than half are pedestrian or cyclist fatalities, highlighting the need to improve nighttime safety for vulnerable road users. Extremely boost in sector investments and startup companies make their names. 3D sensors with fewer compromises towards resolution, fidelity and lacking light condition became cheaper and more available; a trend that LID-LAR instruments. However, this paper considers only work involving consumer cameras, either recorded or real-time and dealing with issues not present in daytime traffic, so it is also most relevant but adaptable to L- L, dAR or any future additions, including imaging fusions from multiple modalities. There are substantial insights in terms of research and applications following from night vision studies themselves, where census for collaboration in nighttime traffic data and night-time traffic simulations was published just slightly earlier than the COVID-19 pandemic started. That seminal effort sets the stage for tricky new night-time simulation, the now proven approach to narrowing the gap between affordable and not overly obvious improvements in low-light imaging and the even repaid wheel after COVID-19 pandemic.
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References
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