650 research outputs found
An Empirical Evaluation of Deep Learning on Highway Driving
Numerous groups have applied a variety of deep learning techniques to
computer vision problems in highway perception scenarios. In this paper, we
presented a number of empirical evaluations of recent deep learning advances.
Computer vision, combined with deep learning, has the potential to bring about
a relatively inexpensive, robust solution to autonomous driving. To prepare
deep learning for industry uptake and practical applications, neural networks
will require large data sets that represent all possible driving environments
and scenarios. We collect a large data set of highway data and apply deep
learning and computer vision algorithms to problems such as car and lane
detection. We show how existing convolutional neural networks (CNNs) can be
used to perform lane and vehicle detection while running at frame rates
required for a real-time system. Our results lend credence to the hypothesis
that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio
A dynamic two-dimensional (D2D) weight-based map-matching algorithm
Existing map-Matching (MM) algorithms primarily localize positioning fixes along the centerline of a road and have largely ignored road width as an input. Consequently, vehicle lane-level localization, which is essential for stringent Intelligent Transport System (ITS) applications, seems difficult to accomplish, especially with the positioning data from low-cost GPS sensors. This paper aims to address this limitation by developing a new dynamic two-dimensional (D2D) weight-based MM algorithm incorporating dynamic weight coefficients and road width. To enable vehicle lane-level localization, a road segment is virtually expressed as a matrix of homogeneous grids with reference to a road centerline. These grids are then used to map-match positioning fixes as opposed to matching on a road centerline as carried out in traditional MM algorithms. In this developed algorithm, vehicle location identification on a road segment is based on the total weight score which is a function of four different weights: (i) proximity, (ii) kinematic, (iii) turn-intent prediction, and (iv) connectivity. Different parameters representing network complexity and positioning quality are used to assign the relative importance to different weight scores by employing an adaptive regression method. To demonstrate the transferability of the developed algorithm, it was tested by using 5,830 GPS positioning points collected in Nottingham, UK and 7,414 GPS positioning points collected in Mumbai and Pune, India. The developed algorithm, using stand-alone GPS position fixes, identifies the correct links 96.1% (for the Nottingham data) and 98.4% (for the Mumbai-Pune data) of the time. In terms of the correct lane identification, the algorithm was found to provide the accurate matching for 84% (Nottingham) and 79% (Mumbai-Pune) of the fixes obtained by stand-alone GPS. Using the same methodology adopted in this study, the accuracy of the lane identification could further be enhanced if the localization data from additional sensors (e.g. gyroscope) are utilized. ITS industry and vehicle manufacturers can implement this D2D map-matching algorithm for liability critical and in-vehicle information systems and services such as advanced driver assistant systems (ADAS)
Standalone and RTK GNSS on 30,000 km of North American Highways
There is a growing need for vehicle positioning information to support
Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Automated
Driving (AD) features. These range from a need for road determination (<5
meters), lane determination (<1.5 meters), and determining where the vehicle is
within the lane (<0.3 meters). This work examines the performance of Global
Navigation Satellite Systems (GNSS) on 30,000 km of North American highways to
better understand the automotive positioning needs it meets today and what
might be possible in the near future with wide area GNSS correction services
and multi-frequency receivers. This includes data from a representative
automotive production GNSS used primarily for turn-by-turn navigation as well
as an Inertial Navigation System which couples two survey grade GNSS receivers
with a tactical grade Inertial Measurement Unit (IMU) to act as ground truth.
The latter utilized networked Real-Time Kinematic (RTK) GNSS corrections
delivered over a cellular modem in real-time. We assess on-road GNSS accuracy,
availability, and continuity. Availability and continuity are broken down in
terms of satellite visibility, satellite geometry, position type (RTK fixed,
RTK float, or standard positioning), and RTK correction latency over the
network. Results show that current automotive solutions are best suited to meet
road determination requirements at 98% availability but are less suitable for
lane determination at 57%. Multi-frequency receivers with RTK corrections were
found more capable with road determination at 99.5%, lane determination at 98%,
and highway-level lane departure protection at 91%.Comment: Accepted for the 32nd International Technical Meeting of the
Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami,
Florida, September 201
- …