Traffic data is required for intelligent transportation systems to function smoothly. The intersections are places that have the greatest potential of conflicts between road users, it is crucial for intelligent transportation systems to dependably and intelligently monitor the different means of traffic.
According to estimates of The Federal Highway Administration, above 50 percent of the combined total of injury and fatal crashes occur at or close to intersections. The intersection is a particularly dangerous place for pedestrians. And when its gets dark earlier in fall and winter, the number of crashes increase dramatically. Thus it requires to be aware of the surroundings in low-visibility conditions for the mobility and safety of all road users.
The use of cameras is a practice of some road safety agencies. But the functioning of cameras is limited in dark, rainy, or foggy conditions. Some cities install radar in place of cameras, which functions better in low-visibility conditions, but has a downside of not being able to provide a rich picture of the situation. Conventional radar provides position and movement data for all approaching users, but it is extremely difficult to provide the difference between modes with reliability.
The development of a high resolution radar sensor that can reliably differentiate between pedestrians and cars has resolved the issue. The sensor also furnishes the speed, count, and direction of each moving target irrespective of how the lighting and weather is like. It is planned to further refine the model to comprehend more complex data and be able to detect additional mode of transport.
Meanwhile, to create the prototype, the research team used a high resolution radar sensor of a millimeter wave that outperforms cameras in low visibility scenarios.