1. A method for vision-centric deep-learning-based road situation analysis, comprising: receiving real-time traffic environment visual input from at least one camera; determining, using a recurrent you only look once (ROLO) engine, at least one initial region of interest from the real-time traffic environment visual input by using a convolutional neural networks (CNN) training method; verifying, using the recurrent you only look once (ROLO) engine, the at least one initial region of interest to determine if a detected object in the at least one initial region of interest is a candidate object to be tracked by using the CNN training method; in response to determining the detected object is a candidate object, tracking, using a plurality of long short-term memory units (LSTMs), the detected object based on the real-time traffic environment visual input, and predicting a future status of the detected object by using the CNN training method; and determining if a warning signal is to be presented to a driver of a vehicle based on the predicted future status of the detected object.