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CNBC AI News reports a recent online video showing a self-driving delivery vehicle encountering a rather unusual obstacle: a woman using a public road to dry peanuts.
The video depicts a woman utilizing one lane of a two-way, four-lane road, and even part of the sidewalk, to spread out her peanut harvest for drying. This effectively reduces the road to three lanes of usable traffic, creating a potential hazard for other drivers.
Typically, drivers would navigate around such an obstruction. However, this time, the situation involved an autonomous delivery vehicle, reportedly belonging to SF Express, one of China’s largest logistics companies.
The video shows the SF Express autonomous vehicle proceeding directly through the peanuts spread across the road. The woman is seen visibly frustrated, gesturing and even striking the vehicle, which continues its route.
Adding a layer of irony, the vehicle issues a polite, automated message stating: “Self-driving mode activated. Please maintain a distance of 2 meters. Ready to stop anytime.”
The incident has sparked online discussions, with some users commenting on the “unwavering” and “rule-abiding” nature of the autonomous vehicle. Others see the situation as highlighting the practical challenges and ethical considerations arising from the deployment of self-driving technology in complex real-world environments.
This event raises questions about the training and programming of autonomous vehicles to handle unforeseen circumstances. While the vehicle adhered to its programmed route, it did so at the expense of potentially damaging personal property and disregarding the implicit social contract of yielding to vulnerable individuals. This incident underscores the need for more nuanced AI decision-making that can better balance rigid adherence to rules with contextual awareness and human consideration. Furthermore, the incident highlights the importance of robust sensor technology and object recognition capabilities to differentiate between legitimate obstacles and temporary obstructions like drying agricultural products. The incident begs the question whether further AI driven contextual analysis could be introduced into the AV operational guidelines which would enable the vehicles to differentiate between an accident, a hard obstacle, or unusual road side activities.
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