Transformation-Equivariant 3D Object Detection for Autonomous Driving

Object Detection for Autonomous Driving Using Deep Learning

3D Laser Based Obstacle Detection for Autonomous Driving

In urban environments, the vehicle encounters a wide variety of static and moving obstacles. Obstacles as small as a curb may trip a fast-moving vehicle, so detecting small objects is of great importance. Overhangs and trees may look large obstacles at a distance, but traveling underneath is often possible. Thus, obstacle detection must consider the 3D geometry of the world. Figure 1 depicts a ...

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...

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)...

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...