Pedestrians are vulnerable road users of the traffic system. Understanding pedestrian behavior patterns when exposed to difficult traffic scenarios can be a challenge. Pedestrian characteristics such as age, gender, static context (weather, time of day, street width) and Time-To-Collision (TTC) and behavioral patterns can be so different when exposed and thus their behavior becomes unpredictable.
Designing autonomous vehicles for urban environments remains an unresolved problem. One major dilemma faced by autonomous cars is understanding the intention of other road users and communicating with them. To investigate one aspect of this, specifically pedestrian crossing behavior, we have collected a large dataset of pedestrian samples at crosswalks under various conditions (e.g. weather) and in different types of roads. Using the data, we analyzed pedestrian behavior from two different perspectives: the way they communicate with drivers prior to crossing and the factors that influence their behavior
Our study shows that changes in head orientation in the form of looking or glancing at the traffic is a strong indicator of crossing intention. Our artificial intelligence solutions will focus upon these problems so as to bring to safety to drivers and to pedestrians.
Idea & Concept
We present a real-time algorithm to learn varying pedestrian behavior characteristics from real-world videos. Our formulation is based on behavior classification using Personality Trait Theory. We present a scheme to dynamically learn the behavior of every pedestrian in the scene and use that to compute its motion parameters and future states. This behavior learning scheme makes no assumptions about prior pedestrian motion or crowd density and uses a precomputed database. We use behavior classification for real-time path prediction and navigation in low and medium density videos with tens of pedestrians.