Driver Analysis and Intent

It is well studied that visual attention patterns and the associated eye movements reflect cognitive processes. There have been many research works on the role of eye movements in executing everyday visually guided behaviors, where eye trackers measurements were used. Our work is focused on learning about how humans visually inspect an scene while their are making driving tasks. It is of our interest to get some clues about whether there are specific places in the scene that are strategic to determine the behaviour, and in which moment the observer pays attention to them. We are using eye tracker equipment as EEG signals to correlate brain processing and eye movement.

 

 

Visual perception algorithms

Visual perception and understanding of the environment with video cameras is a research area in continuous growth. The cameras have greatly favored the use of mobile robots in many applications. The automotive industry, for example, has invested many efforts to provide some intelligence to their vehicles in order to assist the driver in dangerous situations and thus reduce the risk of accidents. The use of cameras has a key role here, since all traffic signs and other related signals are designed exclusively to be seen by people. However, these systems typically have a high degree of dependence on the structure of the roads and must deal with real ambient conditions such as drastic illumination changes, poor visibility conditions and wide range of environments and infrastructure, among other things. Moreover, the human visual system proves to be robust and flexible enough to allow an experienced driver to successfully drive in extreme conditions, attracting new developments inspired in human vision. We are developing new visual perception algorithms to sucesfully extract not only the road structure, but also specific potential risk to the driving tasks.

Simulation Environment for test and visualization

It is not always possible to obtain real data to demonstrate some type of algorithms in transportation systems. For example, the way that a time to collision alarm avoids or not an accident. On the other end, there is a large amount of data that are collected form experiments with intelligente connected vehicles. And, there is not an easy way to see all this data in action. To address these issues, we are experimenting with a game engine platform to develop an enviroment where new algorithms can be tested and at the same time can allow us to show experiments from all the agents in a scene: people, other cars, etc.