OpenSource CiThruS Simulation Environment for RealTime 360Degree Traffic Imaging This paper presents an opensource simulation environment for 360degree traffic imaging. The environment is built on the openly available AirSim Windridge City Asset. In this work, the city is populated with custom autonomous vehicles and pedestrians. The vehicles navigate along a designed node map that can be manually placed on the roads according to the specified traffic regulations. The vehicles are also made to detect other vehicles, pedestrians, and traffic lights for simple collision avoidance and smoother traffic flows in intersections. The pedestrians follow a NavMesh placed on the walkable areas and stop at the traffic lights when crossing the streets. Weather effects, timeofday, and rain distortion lens shader bring the environment more close to the reality. The whole system is built on top of free and selfmade assets, making it easy to use, configure, and extend. The performance of the simulator exceeds 60 frames per second when run on NVIDIA RTX 2070 with Intel Xeon E52620 or equivalent hardware.

Realtime 3D Traffic Cone Detection for Autonomous Driving. Considerable progress has been made in semantic scene understanding of road scenes with monocular cameras. It is, however, mainly related to certain classes such as cars and pedestrians. This work investigates traffic cones, an object class crucial for traffic control in the context of autonomous vehicles. 3D object detection using images from a monocular camera is intrinsically an illposed problem. In this work, we leverage the unique structure of traffic cones and propose a pipelined approach to the problem. Specifically, we first detect cones in images by a tailored 2D object detector; then, the spatial arrangement of keypoints on a traffic cone are detected by our deep structural regression network, where the fact that the crossratio is projection invariant is leveraged for network regularization; finally, the 3D position of cones is recovered by the classical Perspective nPoint algorithm. Extensive experiments show that our approach can accurately detect traffic cones and estimate their position in the 3D world in real time. The proposed method is also deployed on a realtime, critical system. It runs efficiently on the lowpower Jetson TX2, providing accurate 3D position estimates, allowing a racecar to map and drive autonomously on an unseen track indicated by traffic cones. With the help of robust and accurate perception, our racecar won both Formula Student Competitions held in Italy and Germany in 2018, cruising at a topspeed of 54 kmph. Visualization of the complete pipeline, mapping and navigation can be found on our project page.