3D Traffic Simulation for Autonomous Vehicles in Unity and Python
This work provides a tool for researchers in autonomous vehicles to simulate traffic scenarios more efficiently, though it appears incremental as it builds on existing simulation concepts.
The authors tackled the problem of data collection for autonomous vehicle research by developing a 3D traffic simulation that uses real-time position data from street cameras, offering flexibility, time-saving, and scalability compared to traditional methods.
Over the recent years, there has been an explosion of studies on autonomous vehicles. Many collected large amount of data from human drivers. However, compared to the tedious data collection approach, building a virtual simulation of traffic makes the autonomous vehicle research more flexible, time-saving, and scalable. Our work features a 3D simulation that takes in real time position information parsed from street cameras. The simulation can easily switch between a global bird view of the traffic and a local perspective of a car. It can also filter out certain objects in its customized camera, creating various channels for objects of different categories. This provides alternative supervised or unsupervised ways to train deep neural networks. Another advantage of the 3D simulation is its conformation to physical laws. Its naturalness to accelerate and collide prepares the system for potential deep reinforcement learning needs.