ALODAD: An Anchor-Free Lightweight Object Detector for Autonomous Driving

A Lightweight Simulator for Autonomous Driving Motion Planning Development

A good simulation environment will facilitate motion planning algorithm development for urban autonomous driving. The first requirement of such a simulator is to be able to replicate a complex urban environment, including road network, curb, general objects, etc. The second requirement is to simulate a realistic host vehicle, which includes perception, control and vehicle dynamics, to recreate ...

Object Detection for Autonomous Driving Using Deep Learning

Driving an autonomous car with eye tracking

This paper describes eyeDriver, a hardware and software setup to drive an autonomous car with eye movement. The movement of the operator’s iris is tracked with an infrared sensitive camera built onto a HED4 interface by SMI. The position of the iris is then propagated by eyeDriver to control the steering wheel of “Spirit of Berlin”, a completely autonomous car developed by the Free University o...

Secure autonomous driving in dynamic environments: From object detection to safe driving

Secure driving in dynamic environments is an application requiring a number of premises. First of all it needs a perception system able to detect and register obstacles in the vicinity of the robot. Those obstacles are mapped and passed to a motion planner able to calculate a path considering the global objective as well as locally collision free trajectories. Finally, as the calculated path is...

Autonomous Driving and Undergraduates: an Affordable Setup for Teaching Robotics

This work proposes an affordable setup for the application of machine learning to autonomous driving. We discuss the hardware details of the platform, and propose a scenario wherein learning from demonstration is leveraged to teach a vehicle how to drive. Preliminary results are discussed, together with potential targets enabled by the setup.