One of the primary issues of computer vision problems in the automobile industry, particularly in advanced driver assistance systems, is the prediction of pedestrian trajectories. For many applications, including autonomous cars, mobile robotics, and state-of-the-art surveillance systems, the capacity to predict people's future actions on the street is a crucial but difficult-to-implement job. Improvements in sensors and related signal processing technologies now aid the performance of state-of-the-art pedestrian trajectory prediction algorithms. At last, this work reveals the gaps in the literature and suggests future avenues for study.