4,146 research outputs found
Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks
A fundamental challenge in autonomous vehicles is adjusting the steering
angle at different road conditions. Recent state-of-the-art solutions
addressing this challenge include deep learning techniques as they provide
end-to-end solution to predict steering angles directly from the raw input
images with higher accuracy. Most of these works ignore the temporal
dependencies between the image frames. In this paper, we tackle the problem of
utilizing multiple sets of images shared between two autonomous vehicles to
improve the accuracy of controlling the steering angle by considering the
temporal dependencies between the image frames. This problem has not been
studied in the literature widely. We present and study a new deep architecture
to predict the steering angle automatically by using Long-Short-Term-Memory
(LSTM) in our deep architecture. Our deep architecture is an end-to-end network
that utilizes CNN, LSTM and fully connected (FC) layers and it uses both
present and futures images (shared by a vehicle ahead via Vehicle-to-Vehicle
(V2V) communication) as input to control the steering angle. Our model
demonstrates the lowest error when compared to the other existing approaches in
the literature.Comment: Accepted in IV 2019, 6 pages, 9 figure
From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability
Advances in Data Science permeate every field of Transportation Science and Engineering,
resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent
Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and
consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure,
vehicles or the travelers’ personal devices act as sources of data flows that are eventually
fed into software running on automatic devices, actuators or control systems producing, in turn,
complex information flows among users, traffic managers, data analysts, traffic modeling scientists,
etc. These information flows provide enormous opportunities to improve model development and
decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used
to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes;
in other words, for data-based models to fully become actionable. Grounded in this described data
modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic
to its three compounding stages, namely, data fusion, adaptive learning and model evaluation.
We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm
conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying
the majority of ITS applications. Finally, we provide a prospect of current research lines within
Data Science that can bring notable advances to data-based ITS modeling, which will eventually
bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government
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