2,137 research outputs found
Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems
World-wide injuries in vehicle accidents have been on the rise in recent
years, mainly due to driver error. The main objective of this research is to
develop a predictive system for driving maneuvers by analyzing the cognitive
behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle
is being driven). Advanced Driving Assistance Systems (ADAS) include
different driving functions, such as vehicle parking, lane departure warning,
blind spot detection, and so on. While much research has been performed on
developing automated co-driver systems, little attention has been paid to the
fact that the driver plays an important role in driving events. Therefore, it
is crucial to monitor events and factors that directly concern the driver. As
a goal, we perform a quantitative and qualitative analysis of driver behavior
to find its relationship with driver intentionality and driving-related actions.
We have designed and developed an instrumented vehicle (RoadLAB) that is
able to record several synchronized streams of data, including the surrounding
environment of the driver, vehicle functions and driver cephalo-ocular behavior,
such as gaze/head information. We subsequently analyze and study the
behavior of several drivers to find out if there is a meaningful relation between
driver behavior and the next driving maneuver
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
Mapping of Road Facilities and Information on High Definition Maps using Mobile Mapping System
学位の種別: 修士University of Tokyo(東京大学
Visual computing techniques for automated LIDAR annotation with application to intelligent transport systems
106 p.The concept of Intelligent Transport Systems (ITS) refers to the application of communication and information technologies to transport with the aim of making it more efficient, sustainable, and safer. Computer vision is increasingly being used for ITS applications, such as infrastructure management or advanced driver-assistance systems. The latest progress in computer vision, thanks to the Deep Learning techniques, and the race for autonomous vehicle, have created a growing requirement for annotated data in the automotive industry. The data to be annotated is composed by images captured by the cameras of the vehicles and LIDAR data in the form of point clouds. LIDAR sensors are used for tasks such as object detection and localization. The capacity of LIDAR sensors to identify objects at long distances and to provide estimations of their distance make them very appealing sensors for autonomous driving.This thesis presents a method to automate the annotation of lane markings with LIDAR data. The state of the art of lane markings detection based on LIDAR data is reviewed and a novel method is presented. The precision of the method is evaluated against manually annotated data. Its usefulness is also evaluated, measuring the reduction of the required time to annotate new data thanks to the automatically generated pre-annotations. Finally, the conclusions of this thesis and possible future research lines are presented
Visual computing techniques for automated LIDAR annotation with application to intelligent transport systems
106 p.The concept of Intelligent Transport Systems (ITS) refers to the application of communication and information technologies to transport with the aim of making it more efficient, sustainable, and safer. Computer vision is increasingly being used for ITS applications, such as infrastructure management or advanced driver-assistance systems. The latest progress in computer vision, thanks to the Deep Learning techniques, and the race for autonomous vehicle, have created a growing requirement for annotated data in the automotive industry. The data to be annotated is composed by images captured by the cameras of the vehicles and LIDAR data in the form of point clouds. LIDAR sensors are used for tasks such as object detection and localization. The capacity of LIDAR sensors to identify objects at long distances and to provide estimations of their distance make them very appealing sensors for autonomous driving.This thesis presents a method to automate the annotation of lane markings with LIDAR data. The state of the art of lane markings detection based on LIDAR data is reviewed and a novel method is presented. The precision of the method is evaluated against manually annotated data. Its usefulness is also evaluated, measuring the reduction of the required time to annotate new data thanks to the automatically generated pre-annotations. Finally, the conclusions of this thesis and possible future research lines are presented
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Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications
The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data
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