One major contrast between human drivers and self-driving cars is latter’s ability to comprehend the risk in form of mathematical formulation, while the former uses the cases of intuition and past experiences.
Primarily, a self-driving car uses two considerations while analyzing the probability for any decision on driving, one of them is the ability to collect correct data and the second one being the utilization of that data in a correct algorithm for appropriate conclusion extraction. To escalate the performance of such a model, data collection certainly provides a crucial hurdle as the entire working depends on it.
While more probabilistic approaches are being implied for enhancing the performance of self-driving cars, companies like Ford have announced the arrival of their self-driving models on road by 2021. With such fast-paced technological approach startling us each day probability of driving safe will be redefined by these latest models.
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