Knowing what pedestrians will do next is a major challenge for the autonomous vehicle industry. At INTVO, we’re driving progress in this area, by collecting and utilizing a large dataset of pedestrian samples. Using this data, we’re able to understand behavior from two perspectives:
- The subtle movements pedestrians use to communicate with drivers
- Factors that influence pedestrian behavior
Using this intelligence, our systems anticipate the behavior of pedestrians, cyclists, motorcyclists –even animals and pets, all under various road and weather conditions.
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 the 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.