Towards Visual Urban Scene Understanding For Autonomous Vehicle Path Tracking Using Gps Positioning Data
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Towards Visual Urban Scene Understanding for Autonomous Vehicle Path Tracking Using GPS Positioning Data
This PhD thesis focuses on developing a path tracking approach based on visual perception and localization in urban environments. The proposed approach comprises two systems. The first one concerns environment perception. This task is carried out using deep learning techniques to automatically extract 2D visual features and use them to learn in order to distinguish the different objects in the driving scenarios. Three deep learning techniques are adopted: semantic segmentation to assign each image pixel to a class, instance segmentation to identify separated instances of the same class and, image classification to further recognize the specific labels of the instances. Here our system segments 15 object classes and performs traffic sign recognition. The second system refers to path tracking. In order to follow a path, the equipped vehicle first travels and records the route with a stereo vision system and a GPS receiver (learning step). The proposed system analyses off-line the GPS path and identifies exactly the locations of dangerous (sharp) curves and speed limits. Later after the vehicle is able to localize itself, the vehicle control module together with our speed negotiation algorithm, takes into account the information extracted and computes the ideal speed to execute. Through experimental results of both systems, we prove that, the first one is capable to detect and recognize precisely objects of interest in urban scenarios, while the path tracking one reduces significantly the lateral errors between the learned and traveled path. We argue that the fusion of both systems will ameliorate the tracking approach for preventing accidents or implementing autonomous driving.
The DARPA Urban Challenge
By the dawn of the new millennium, robotics has undergone a major transformation in scope and dimensions. This expansion has been brought about by the maturity of the field and the advances in its related technologies. From a largely dominant industrial focus, robotics has been rapidly expanding into the challenges of the human world. The new generation of robots is expected to safely and dependably co-habitat with humans in homes, workplaces, and communities, providing support in services, entertainment, education, healthcare, manufacturing, and assistance. Beyond its impact on physical robots, the body of knowledge robotics has produced is revealing a much wider range of applications reaching across diverse research areas and scientific disciplines, such as: biomechanics, haptics, neurosciences, virtual simulation, animation, surgery, and sensor networks among others. In return, the challenges of the new emerging areas are proving an abundant source of stimulation and insights for the field of robotics. It is indeed at the intersection of disciplines that the most striking advances happen. The goal of the series of Springer Tracts in Advanced Robotics (STAR) is to bring, in a timely fashion, the latest advances and developments in robotics on the basis of their significance and quality. It is our hope that the wider dissemination of research developments will stimulate more exchanges and collaborations among the research community and contribute to further advancement of this rapidly growing field.
Autonomous Ground Vehicles
In the near future, we will witness vehicles with the ability to provide drivers with several advanced safety and performance assistance features. Autonomous technology in ground vehicles will afford us capabilities like intersection collision warning, lane change warning, backup parking, parallel parking aids, and bus precision parking. Providing you with a practical understanding of this technology area, this innovative resource focuses on basic autonomous control and feedback for stopping and steering ground vehicles.Covering sensors, estimation, and sensor fusion to percept the vehicle motion and surrounding objects, this unique book explains the key aspects that makes autonomous vehicle behavior possible. Moreover, you find detailed examples of fusion and Kalman filtering. From maps, path planning, and obstacle avoidance scenarios...to cooperative mobility among autonomous vehicles, vehicle-to-vehicle communication, and vehicle-to-infrastructure communication, this forward-looking book presents the most critical topics in the field today.