AI Poses One Clear Threat To The Future Of Autonomous Vehicles



The likes of Tesla try to address those unprecedented scenarios with a system called detectors, which learns from visual data collected from a car and then it is beamed back as an iteration. However, despite all those efforts and domain-leading computing prowess, Tesla cars are also involved in the highest number of accidents with driver-assist systems enabled. Mears thinks carmakers should also look at a few other aspects instead of just simulation modeling and training on driver data.

“OEMs have recently transitioned away from rule-based systems, but if we don’t respect the rules of the road absolutely (i.e., laws and regulations), what kind of driving system would result and how effective and predictable could it be?,” Mears tells SlashGear. It’s a solid argument and one that echoes Tesla’s own policies. The company notes that drivers should always be ready to take over the steering wheel, irrespective of whether the self-driving system is engaged or not.

The problem, according to Mears, lies in how AI acquires its decision-making skills. “AI can learn through observation, but it needs to have a clear understanding of right and wrong in order to make the best decisions, like a person,” he tells us. Multiple research papers have highlighted similar risks from “unknown unknowns” and how a shift in training strategy is necessary. “Higher-level AVs require a self-driving system that incorporates a decision-making ability capable of dealing with unknown driving scenes and uncertainty,” says a research paper published in the Sensors journal.

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