Abstract:

To support the optimal selection of public testing road sites and provide instructions for improving driving environments for autonomous vehicles, a quantitative evaluation method for driving environment visual perception complexity was proposed. Based on the driving environment dataset from Baidu street view map, an automated extraction platform of street view image data was established using a script file and the screenshot tool PicPick. Driving environment data of 50 typical Shanghai roads of different areas and different road grades were collected. Then, an element perception platform of driving environment was established from five aspects—pedestrians, traffic signs, road markings, traffic lights, and vehicles—to quantitatively evaluate perception accuracy. Based on single-element perception accuracy, the weights for multidimensional perception elements were determined using the entropy method, and integrated perception accuracy at the road level was calculated. The silhouette coefficient method and K-means++ clustering algorithm were used to propose the classification of visual perception complexity. Results showed that the visual perception complexity of the road network in Shanghai can be divided into three levels. The majority of driving environment visual perception complexities of roads belong to level 2. In addition, comparisons among roads of different grades indicated that the visual perception complexity of local roads is generally lower than that of arterial roads.

Key words: :visual perception complexity, complexity classification, perception key elements, driving environment, autonomous vehicles