A Camera-Only Based Approach to Traffic Parameter Estimation Using Mobile Observer Methods

As vehicles become more modern, a large majority of vehicles on the road will have the required sensors to smoothly interact with other vehicles and infrastructure on the road. There will be many benefits of this new connectivity between vehicles on the road but one of the most profound improvements will be in the area of road accident prevention. Vehicles will be able to share information vital to road safety to oncoming vehicles and vehicles that are occluded so they do not have a direct line of sight to see a pedestrian or another vehicle on the road. Another advantage of these modern connected vehicles is that different traffic parameters can be more easily estimated using the onboard sensors and technologies in the vehicles. For many decades traffic engineers have been able to estimate different traffic parameters like traffic flow, density, and velocity based on how many vehicles the primary vehicle passes and how many vehicles pass the primary vehicles. For much of the time that traffic engineers have been working on traffic estimation, it has been done using more manual and tedious methods. In this paper, a more novel approach of determining these traffic parameters is used. Also, one of the problems with traffic parameter estimation is that sometimes the results are not accurate because of vehicles that might not have been counted because of occlusion. In this paper, a proposal is put forward on how this can be remedied utilizing the connected vehicle\u27s framework

Studies on Next-Gen Vehicular Detection: A Fusion of RF Signal and Fisheye Camera Technologies

早稲田大学修士(工学)master thesi

End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners

For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a more complete view of what is happening around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. We investigate the problem in a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such a sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather/illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: 1) by representing the planned routes on OpenStreetMap as a stack of GPS coordinates, and 2) by rendering the planned routes on TomTom Go Mobile and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera, in particular for city driving and intersection scenarios; and 2) route planners help the driving task significantly, especially for steering angle prediction.Comment: to be published at ECCV 201