Robocart Machine Vision Framework

This project documents a framework for an extensible, flexible machine vision software implementation for the Robocart project. It uses a distributed mobile computing framework in order to best leverage the scalability of machine vision. This process aims to improve upon current machine vision implementations in commercial autonomous vehicles, as well as provide a basis for further development of RobocartÂ’s autonomous navigation systems. This framework is tested with the use case of road detection

Robocart: System Design for the First Generation Autonomous Golf Cart

Inspired by ongoing research and continuous developments in autonomous vehicles, the Robocart MQP focuses on the system development for a first-generation autonomous golf cart vehicle and wireless system server. By creating the foundation for a modular and interdisciplinary system, visualization software and mechanisms can be intuitively integrated. The end result of this project is a better understanding of the efficiency of each subsystems against the real-time challenges required for an autonomous, wireless, and vision-based system. In conclusion of this project, recommendations in mechanical, electrical, and algorithm development were formed to promote further research and enhance rider usability

Automated Driving and its Effect on the Safety Ecosystem: How do Compatibility Issues Affect the Transition Period?

AbstractDifferent components of automated vehicles are being made available commercially as we speak. Much research has been conducted into these components and many of these have been studied with respect to their effects on safety, but the transition period from non-automated driving to fully automated vehicles raises safety related issues dealing with mixed traffic situations. More in-depth knowledge should be gained in (the safety of) the behaviour of drivers of unequipped vehicles, enabling automated vehicles to predict and adequately respond to potentially unsafe behaviour, a concept we call backwards compatibility. Also, automated vehicle system design tends to be from an optimal system performance perspective which leads to driving patterns such as driving in the centre of a lane. Other (human) road users however likely exhibit driving behaviour in line with different rationales which allow for suboptimal driving patterns. As of yet, it remains unclear whether these patterns contain indications about the intentions of a driver and if or how other road users anticipate these. This could have two consequences with regard to mixed traffic situations. First of all, other road users might miss important cues from the behaviour of the automated vehicle (what we call forward incompatibility). Secondly, the occupant of an automated vehicle might expect human-like behaviour from the automated vehicle in safety-critical situations, lowering acceptance if this does not meet expectations. The current paper considers these issues and states that we need more insight in how road users use other road users’ behaviour to anticipate safety critical events, especially in the transition period towards fully automated vehicles

Editorial for special issue on Perception and Navigation for Autonomous Vehicles

International audienceThis Special Issue of the IEEE Robotics and Automation Magazine has been prepared in the scope of the activities of the Technical Committee on "Autonomous Ground Vehicle and Intelligent Transportation System" (AGV-ITS) (http://www.ieee-ras.org/autonomous-groundvehicles- and-intelligent-transportation-systems) of the IEEE Robotics and Automation Society (IEEE RAS)

A Review of Sensor Technologies for Perception in Automated Driving

After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show future market trends in sensors technologies for Automated Vehicles.This work has been partly supported by ECSEL Project ENABLE- S3 (with grant agreement number 692455-2), by the Spanish Government through CICYT projects (TRA2015- 63708-R and TRA2016-78886-C3-1-R)

3D Vision-based Perception and Modelling techniques for Intelligent Ground Vehicles

In this work the candidate proposes an innovative real-time stereo vision system for intelligent/autonomous ground vehicles able to provide a full and reliable 3D reconstruction of the terrain and the obstacles. The terrain has been computed using rational B-Splines surfaces performed by re-weighted iterative least square fitting and equalization. The cloud of 3D points, generated by the processing of the Disparity Space Image (DSI), is sampled into a 2.5D grid map; then grid points are iteratively fitted into rational B-Splines surfaces with different patterns of control points and degrees, depending on traversability consideration. The obtained surface also represents a segmentation of the initial 3D points into terrain inliers and outliers. As final contribution, a new obstacle detection approach is presented, combined with terrain estimation system, in order to model stationary and moving objects in the most challenging scenarios