Comparison of the Pedestrian Crossing Intention Parameters and Research on Intention Recognition Model Under Different Road Conditions

Abstract

There have a large number of pedestrian-vehicle accidents on the pedestrian crossing area in China every year, causing huge loss of life and property. In view of different road conditions, it's crucial to establish a more accurate crossing intention recognition model to improve the safety of pedestrians. In this work, a pedestrian crossing area was chosen. Due to construction reasons, two road conditions appeared in the same crossing area at different periods, namely a condition with a zebra crossing and that without a zebra crossing. We compared pedestrian crossing intention parameters under two road conditions in the same crossing area. The results found that there was a great difference in the characterization parameters of pedestrian crossing intention when the site with and without a zebra crossing. Additionally, a more comprehensive crossing intention characteristic parameters set was established. The characteristic parameters were pedestrian speed, the distance between vehicle and crossing area, time to collision (TTC), and safe vehicle deceleration (SVD), pedestrian age, pedestrian gender, group, respectively. The pedestrian intention recognition model for the site with a and without a zebra crossing were established by long short-term memory network integrated with the attention mechanism (AT-LSTM). When the model recognized pedestrian crossing intention 0.6 seconds in advance, the recognition accuracies were 93.05% and 93.89% respectively. The research results are of great significance for improving the safety of autonomous vehicles in the future, and there are also important to improve pedestrian safety.

Design, Implementation and Experimental Investigation of a Pedestrian Street Crossing Assistance System Based on Visible Light Communications

“…At the present time, most of the existing solutions focused on pedestrian presence are based on camera systems that use different video processing algorithms to detect pedestrians and estimate their future street crossing intention [ 62 , 63 , 64 , 65 ]. The probability of a pedestrian crossing or not is generally based on the analyses of the pedestrian’s posture and on the analysis of the posture changes in multiple frame images.…”

“…The performances of such systems are currently being improved with the help of neural networks [ 66 ] or adversarial feature learning [ 67 ], which are orientated to establish the current pedestrian orientation [ 68 ] and the probability of future action [ 69 ]. Such systems can predict a future pedestrian’s street crossing intention with 0.6 s in advance while providing recognition accuracies higher than 93% [ 65 ]. Nevertheless, the performance of such solutions could be affected by the vehicle movement, involving in turn a constant rescaling process [ 70 ].…”

Innovative Framework for Distracted-Driving Alert System Based on Deep Learning

“…In terms of pedestrian safety, TTC is an important criterion for collision prevention to improve pedestrian safety. Wang et al [65] used attention LSTM to predict pedestrian crossing and TTC to estimate time gap between pedestrian and vehicle for improving pedestrian crossing safety. Jiang et al [66] combined TTC, video, and local cultural information for pedestrian midblock crosswalk safety analysis.…”