27,593 research outputs found
Teaching Autonomous Driving Using a Modular and Integrated Approach
Autonomous driving is not one single technology but rather a complex system
integrating many technologies, which means that teaching autonomous driving is
a challenging task. Indeed, most existing autonomous driving classes focus on
one of the technologies involved. This not only fails to provide a
comprehensive coverage, but also sets a high entry barrier for students with
different technology backgrounds. In this paper, we present a modular,
integrated approach to teaching autonomous driving. Specifically, we organize
the technologies used in autonomous driving into modules. This is described in
the textbook we have developed as well as a series of multimedia online
lectures designed to provide technical overview for each module. Then, once the
students have understood these modules, the experimental platforms for
integration we have developed allow the students to fully understand how the
modules interact with each other. To verify this teaching approach, we present
three case studies: an introductory class on autonomous driving for students
with only a basic technology background; a new session in an existing embedded
systems class to demonstrate how embedded system technologies can be applied to
autonomous driving; and an industry professional training session to quickly
bring up experienced engineers to work in autonomous driving. The results show
that students can maintain a high interest level and make great progress by
starting with familiar concepts before moving onto other modules
Reliable and Efficient Autonomous Driving: the Need for Heterogeneous Vehicular Networks
Autonomous driving technology has been regarded as a promising solution to
reduce road accidents and traffic congestion, as well as to optimize the usage
of fuel and lane. Reliable and high efficient Vehicle-to-Vehicle (V2V) and
Vehicle-to-Infrastructure (V2I) communications are essential to let commercial
autonomous driving vehicles be on the road before 2020. The current paper
firstly presents the concept of Heterogeneous Vehicular NETworks (HetVNETs) for
autonomous driving, in which an improved protocol stack is proposed to satisfy
the communication requirements of not only safety but also non-safety services.
We then consider and study in detail several typical scenarios for autonomous
driving. In order to tackle the potential challenges raised by the autonomous
driving vehicles in HetVNETs, new techniques from transmission to networking
are proposed as potential solutions
Self Training Autonomous Driving Agent
Intrinsically, driving is a Markov Decision Process which suits well the
reinforcement learning paradigm. In this paper, we propose a novel agent which
learns to drive a vehicle without any human assistance. We use the concept of
reinforcement learning and evolutionary strategies to train our agent in a 2D
simulation environment. Our model's architecture goes beyond the World Model's
by introducing difference images in the auto encoder. This novel involvement of
difference images in the auto-encoder gives better representation of the latent
space with respect to the motion of vehicle and helps an autonomous agent to
learn more efficiently how to drive a vehicle. Results show that our method
requires fewer (96% less) total agents, (87.5% less) agents per generations,
(70% less) generations and (90% less) rollouts than the original architecture
while achieving the same accuracy of the original
Augmented LiDAR Simulator for Autonomous Driving
In Autonomous Driving (AD), detection and tracking of obstacles on the roads
is a critical task. Deep-learning based methods using annotated LiDAR data have
been the most widely adopted approach for this. Unfortunately, annotating 3D
point cloud is a very challenging, time- and money-consuming task. In this
paper, we propose a novel LiDAR simulator that augments real point cloud with
synthetic obstacles (e.g., cars, pedestrians, and other movable objects).
Unlike previous simulators that entirely rely on CG models and game engines,
our augmented simulator bypasses the requirement to create high-fidelity
background CAD models. Instead, we can simply deploy a vehicle with a LiDAR
scanner to sweep the street of interests to obtain the background point cloud,
based on which annotated point cloud can be automatically generated. This
unique "scan-and-simulate" capability makes our approach scalable and
practical, ready for large-scale industrial applications. In this paper, we
describe our simulator in detail, in particular the placement of obstacles that
is critical for performance enhancement. We show that detectors with our
simulated LiDAR point cloud alone can perform comparably (within two percentage
points) with these trained with real data. Mixing real and simulated data can
achieve over 95% accuracy.Comment: 10 page
Deep Reinforcement Learning for Autonomous Driving
Reinforcement learning has steadily improved and outperform human in lots of
traditional games since the resurgence of deep neural network. However, these
success is not easy to be copied to autonomous driving because the state spaces
in real world are extreme complex and action spaces are continuous and fine
control is required. Moreover, the autonomous driving vehicles must also keep
functional safety under the complex environments. To deal with these
challenges, we first adopt the deep deterministic policy gradient (DDPG)
algorithm, which has the capacity to handle complex state and action spaces in
continuous domain. We then choose The Open Racing Car Simulator (TORCS) as our
environment to avoid physical damage. Meanwhile, we select a set of appropriate
sensor information from TORCS and design our own rewarder. In order to fit DDPG
algorithm to TORCS, we design our network architecture for both actor and
critic inside DDPG paradigm. To demonstrate the effectiveness of our model, We
evaluate on different modes in TORCS and show both quantitative and qualitative
results.Comment: no time for further improvemen
Fast Scene Understanding for Autonomous Driving
Most approaches for instance-aware semantic labeling traditionally focus on
accuracy. Other aspects like runtime and memory footprint are arguably as
important for real-time applications such as autonomous driving. Motivated by
this observation and inspired by recent works that tackle multiple tasks with a
single integrated architecture, in this paper we present a real-time efficient
implementation based on ENet that solves three autonomous driving related tasks
at once: semantic scene segmentation, instance segmentation and monocular depth
estimation. Our approach builds upon a branched ENet architecture with a shared
encoder but different decoder branches for each of the three tasks. The
presented method can run at 21 fps at a resolution of 1024x512 on the
Cityscapes dataset without sacrificing accuracy compared to running each task
separately.Comment: Published at "Deep Learning for Vehicle Perception", workshop at the
IEEE Symposium on Intelligent Vehicles 201
PI-Edge: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services
To simultaneously enable multiple autonomous driving services on affordable
embedded systems, we designed and implemented {\pi}-Edge, a complete edge
computing framework for autonomous robots and vehicles. The contributions of
this paper are three-folds: first, we developed a runtime layer to fully
utilize the heterogeneous computing resources of low-power edge computing
systems; second, we developed an extremely lightweight operating system to
manage multiple autonomous driving services and their communications; third, we
developed an edge-cloud coordinator to dynamically offload tasks to the cloud
to optimize client system energy consumption. To the best of our knowledge,
this is the first complete edge computing system of a production autonomous
vehicle. In addition, we successfully implemented {\pi}-Edge on a Nvidia Jetson
and demonstrated that we could successfully support multiple autonomous driving
services with only 11 W of power consumption, and hence proving the
effectiveness of the proposed {\pi}-Edge system
Semantic Label Reduction Techniques for Autonomous Driving
Semantic segmentation maps can be used as input to models for maneuvering the
controls of a car. However, not all labels may be necessary for making the
control decision. One would expect that certain labels such as road lanes or
sidewalks would be more critical in comparison with labels for vegetation or
buildings which may not have a direct influence on the car's driving decision.
In this appendix, we evaluate and quantify how sensitive and important the
different semantic labels are for controlling the car. Labels that do not
influence the driving decision are remapped to other classes, thereby
simplifying the task by reducing to only labels critical for driving of the
vehicle
Deep Reinforcement Learning framework for Autonomous Driving
Reinforcement learning is considered to be a strong AI paradigm which can be
used to teach machines through interaction with the environment and learning
from their mistakes. Despite its perceived utility, it has not yet been
successfully applied in automotive applications. Motivated by the successful
demonstrations of learning of Atari games and Go by Google DeepMind, we propose
a framework for autonomous driving using deep reinforcement learning. This is
of particular relevance as it is difficult to pose autonomous driving as a
supervised learning problem due to strong interactions with the environment
including other vehicles, pedestrians and roadworks. As it is a relatively new
area of research for autonomous driving, we provide a short overview of deep
reinforcement learning and then describe our proposed framework. It
incorporates Recurrent Neural Networks for information integration, enabling
the car to handle partially observable scenarios. It also integrates the recent
work on attention models to focus on relevant information, thereby reducing the
computational complexity for deployment on embedded hardware. The framework was
tested in an open source 3D car racing simulator called TORCS. Our simulation
results demonstrate learning of autonomous maneuvering in a scenario of complex
road curvatures and simple interaction of other vehicles.Comment: Reprinted with permission of IS&T: The Society for Imaging Science
and Technology, sole copyright owners of Electronic Imaging, Autonomous
Vehicles and Machines 201
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
The use of object detection algorithms is becoming increasingly important in
autonomous vehicles, and object detection at high accuracy and a fast inference
speed is essential for safe autonomous driving. A false positive (FP) from a
false localization during autonomous driving can lead to fatal accidents and
hinder safe and efficient driving. Therefore, a detection algorithm that can
cope with mislocalizations is required in autonomous driving applications. This
paper proposes a method for improving the detection accuracy while supporting a
real-time operation by modeling the bounding box (bbox) of YOLOv3, which is the
most representative of one-stage detectors, with a Gaussian parameter and
redesigning the loss function. In addition, this paper proposes a method for
predicting the localization uncertainty that indicates the reliability of bbox.
By using the predicted localization uncertainty during the detection process,
the proposed schemes can significantly reduce the FP and increase the true
positive (TP), thereby improving the accuracy. Compared to a conventional
YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average
precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD)
datasets, respectively. Nevertheless, the proposed algorithm is capable of
real-time detection at faster than 42 frames per second (fps) and shows a
higher accuracy than previous approaches with a similar fps. Therefore, the
proposed algorithm is the most suitable for autonomous driving applications.Comment: ICCV 201
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