Mobilenetv2_CA Lightweight Object Detection Network in Autonomous Driving

Object Detection for Autonomous Driving Using Deep Learning

MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving

For autonomous driving, moving objects like vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of the car. Typically, they are detected by motion segmentation of dense optical flow augmented by a CNN based object detector for capturing semantics. In this paper, our aim is to jointly model motion and appearance cues in a single convolution...

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...

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 ...

GPU-based Pedestrian Detection for Autonomous Driving

We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPUCPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for candidate generation; and Support Vector Machine (SVM)...