Predictive Analytics in Self-Driving Cars

 

Forecasting is the sub-set of predictive analytics. Forecasting has the element of time in the data. Historical time series data is required to build a forecasting model. Combining these two elements, predictive analytics is the basis of self-driving car innovation. The application of prediction to clone human behavior in self-driving cars the common “approach to mimic the actions of human drivers” (Gu, Li, Di, & Shi, 2020, para.1).   

Unlike humans who are relying on previous and current experience, self-driving cars rely on previous and current data to learned from and construct patterns. Gained information are input instructions to safely “drive” cars in the simple and complex car driving environments. Self-driving cars, therefore, are trained using “training data” to predict future events on the road. Self-driving cars use machine learning algorithms that are able to take data from different sources (including reading maps) to predict upcoming road trajectories.

Built using Neural Network algorithm, PredctionNet enables us to gather data. Perception algorithms are based on convolutional neural networks, powered by machine learning, a type of deep learning” (Computer, 2019, p.1). In a recent development for better accuracy of prediction, a newer Neural Network algorithm, ResNet, is developed to increase “the depth of the network by adding residual blocks and reached a stunning depth of 152 layers” (Gu, Li, Di,, & Shi, 2020, para.9).   PredictionNet takes the data from the road and predicts upcoming road interactions. Predicting future events comes with uncertainties. PreductionNet enables to report the uncertainties in statistical terms, which are associated with individual road users.

Blog Post: profdetails.blogspot.com

References

Computer, E. (2019). How to make self-driving cars safer on roads. Express Computer, Retrieved from https://proxy.cecybrary.com/login?url=https://www-proquest-com.proxy.cecybrary.com/trade-journals/how-make-self-driving-cars-safer-on-roads/docview/2199220036/se-2?accountid=144789

Gu, Z., Li, Z., Di, X., & Shi, R. (2020). An LSTM-based autonomous driving model using a waymo open dataset. Applied Sciences, 10(6), 2046. doi:http://dx.doi.org.proxy.cecybrary.com/10.3390/app10062046

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