Robust Object Detection Model for Autonomous Driving in Real Scenarios

Real-Time Object Detection for Autonomous Robots

A new kind of robots whose characteristics, objectives and operational modes drastically differ from more conventional industrial robots is gaining increased interest. This new type of robots aims to achieve a high level of flexibility, adaptability, and efficiency for acting in environments designed for humans. To meet these demands, the mobile robots need lots of information about their surro...

Object Detection for Autonomous Driving Using Deep Learning

Robust Real-time Object Detection

This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on Ada...

Real-time camera based lane detection for autonomous driving

In this report a new designed real-time lane detection algorithm based on monocular vision and inertial measurement unit (IMU) is explained and evaluated. The acquired image from the camera is undergoing an inverse perspective mapping to create a bird-view perspective of the road. Local maxima in this image determine the lane-markers. Faulty local maxima are removed and constrained line fitting...

A real-time recursive dynamic model for vehicle driving simulators

This paper presents the Real-Time Recursive Dynamics (RTRD) model that is developed for driving simulators. The model could be implemented in the Driving Simulator. The RTRD can also be used for off-line high-speed dynamics analysis, compared with commercial multibody dynamics codes, to speed up mechanical design process. An overview of RTRD is presented in the paper. Basic models for specific ...