Cyber-physical system enabled nearby traffic flow modelling for autonomous vehicles

We propose a nearby traffic flow modelling solution based on built-in Cyber-Physical System (CPS) sensors of autonomous vehicles. Our goal is to enhance the offline route planning and driving decision adjustment based on the first-hand traffic information, especially during poor Internet connection moments. Specifically, our model helps to select the optimal speed on a road, the optimal distance for timing to brake, and the safe distance from other vehicles to keep. Moreover, our model can also assist neighboring autonomous vehicles by communicating required information through Ad-Hoc network communications or through a centralized cloud. In detail, we first focus on the unique characteristic of traffic flow (such as traffic rule, avoid collision behaviours), and then build a comprehensive model to handle multiple scenarios. Technically, our model uses density functions of velocities, the differential equation of traffic flows, and the traffic viscosity with information collected from the traffic flow, the distances between vehicles, the amount and density of vehicle, the instant velocity, the speed limit, and the momentum to analysis the the driving scene. We evaluate our model with real traffic data collected by in-vehicle CPS sensors to the proposed nearby traffic flow model. Results show that our work can accurately conduct offline estimation on nearby traffic signal influence, and reveal the correlations among velocity, density and (spatial and temporal) location to adjust route during runtime.