26,560 research outputs found
Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
Testing and evaluation is a critical step in the development and deployment
of connected and automated vehicles (CAVs), and yet there is no systematic
framework to generate testing scenario library. This study aims to provide a
general framework for the testing scenario library generation (TSLG) problem
with different operational design domains (ODDs), CAV models, and performance
metrics. Given an ODD, the testing scenario library is defined as a critical
set of scenarios that can be used for CAV test. Each testing scenario is
evaluated by a newly proposed measure, scenario criticality, which can be
computed as a combination of maneuver challenge and exposure frequency. To
search for critical scenarios, an auxiliary objective function is designed, and
a multi-start optimization method along with seed-filling is applied. The
proposed framework is theoretically proved to obtain accurate evaluation
results with much fewer number of tests, if compared with the on-road test
method. In part II of the study, three case studies are investigated to
demonstrate the proposed methodologies. Reinforcement learning based technique
is applied to enhance the searching method under high-dimensional scenarios.Comment: 11 pages,3 figure
Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction
The study and modeling of driver's gaze dynamics is important because, if and
how the driver is monitoring the driving environment is vital for driver
assistance in manual mode, for take-over requests in highly automated mode and
for semantic perception of the surround in fully autonomous mode. We developed
a machine vision based framework to classify driver's gaze into context rich
zones of interest and model driver's gaze behavior by representing gaze
dynamics over a time period using gaze accumulation, glance duration and glance
frequencies. As a use case, we explore the driver's gaze dynamic patterns
during maneuvers executed in freeway driving, namely, left lane change
maneuver, right lane change maneuver and lane keeping. It is shown that
condensing gaze dynamics into durations and frequencies leads to recurring
patterns based on driver activities. Furthermore, modeling these patterns show
predictive powers in maneuver detection up to a few hundred milliseconds a
priori
Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies
Testing scenario library generation (TSLG) is a critical step for the
development and deployment of connected and automated vehicles (CAVs). In Part
I of this study, a general methodology for TSLG is proposed, and theoretical
properties are investigated regarding the accuracy and efficiency of CAV
evaluation. This paper aims to provide implementation examples and guidelines,
and to enhance the proposed methodology under high-dimensional scenarios. Three
typical cases, including cut-in, highway-exit, and car-following, are designed
and studied in this paper. For each case, the process of library generation and
CAV evaluation is elaborated. To address the challenges brought by high
dimensions, the proposed methodology is further enhanced by reinforcement
learning technique. For all three cases, results show that the proposed methods
can accelerate the CAV evaluation process by multiple magnitudes with same
evaluation accuracy, if compared with the on-road test method.Comment: 12 pages, 13 figure
Predicting Lane Keeping Behavior of Visually Distracted Drivers Using Inverse Suboptimal Control
Driver distraction strongly contributes to crash-risk. Therefore, assistance
systems that warn the driver if her distraction poses a hazard to road safety,
promise a great safety benefit. Current approaches either seek to detect
critical situations using environmental sensors or estimate a driver's
attention state solely from her behavior. However, this neglects that driving
situation, driver deficiencies and compensation strategies altogether determine
the risk of an accident. This work proposes to use inverse suboptimal control
to predict these aspects in visually distracted lane keeping. In contrast to
other approaches, this allows a situation-dependent assessment of the risk
posed by distraction. Real traffic data of seven drivers are used for
evaluation of the predictive power of our approach. For comparison, a baseline
was built using established behavior models. In the evaluation our method
achieves a consistently lower prediction error over speed and track-topology
variations. Additionally, our approach generalizes better to driving speeds
unseen in training phase.Comment: 7 pages, 6 figures, accepted for 2016 IEEE Intelligent Vehicles
Symposiu
Deep Multi-Sensor Lane Detection
Reliable and accurate lane detection has been a long-standing problem in the
field of autonomous driving. In recent years, many approaches have been
developed that use images (or videos) as input and reason in image space. In
this paper we argue that accurate image estimates do not translate to precise
3D lane boundaries, which are the input required by modern motion planning
algorithms. To address this issue, we propose a novel deep neural network that
takes advantage of both LiDAR and camera sensors and produces very accurate
estimates directly in 3D space. We demonstrate the performance of our approach
on both highways and in cities, and show very accurate estimates in complex
scenarios such as heavy traffic (which produces occlusion), fork, merges and
intersections.Comment: IEEE International Conference on Intelligent Robots and Systems
(IROS) 201
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
With recent advances in learning algorithms and hardware development,
autonomous cars have shown promise when operating in structured environments
under good driving conditions. However, for complex, cluttered and unseen
environments with high uncertainty, autonomous driving systems still frequently
demonstrate erroneous or unexpected behaviors, that could lead to catastrophic
outcomes. Autonomous vehicles should ideally adapt to driving conditions; while
this can be achieved through multiple routes, it would be beneficial as a first
step to be able to characterize Driveability in some quantified form. To this
end, this paper aims to create a framework for investigating different factors
that can impact driveability. Also, one of the main mechanisms to adapt
autonomous driving systems to any driving condition is to be able to learn and
generalize from representative scenarios. The machine learning algorithms that
currently do so learn predominantly in a supervised manner and consequently
need sufficient data for robust and efficient learning. Therefore, we also
perform a comparative overview of 45 public driving datasets that enable
learning and publish this dataset index at
https://sites.google.com/view/driveability-survey-datasets. Specifically, we
categorize the datasets according to use cases, and highlight the datasets that
capture complicated and hazardous driving conditions which can be better used
for training robust driving models. Furthermore, by discussions of what driving
scenarios are not covered by existing public datasets and what driveability
factors need more investigation and data acquisition, this paper aims to
encourage both targeted dataset collection and the proposal of novel
driveability metrics that enhance the robustness of autonomous cars in adverse
environments
Developing a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping
Our solution is implemented in and for the frame of Duckietown. The goal of
Duckietown is to provide a relatively simple platform to explore, tackle and
solve many problems linked to autonomous driving. "Duckietown" is simple in the
basics, but an infinitely expandable environment. From controlling single
driving Duckiebots until complete fleet management, every scenario is possible
and can be put into practice. So far, none of the existing modules was capable
of reliably detecting obstacles and reacting to them in real time. We faced the
general problem of detecting obstacles given images from a monocular RGB camera
mounted at the front of our Duckiebot and reacting to them properly without
crashing or erroneously stopping the Duckiebot. Both, the detection as well as
the reaction have to be implemented and have to run on a Raspberry Pi in real
time. Due to the strong hardware limitations, we decided to not use any
learning algorithms for the obstacle detection part. As it later transpired, a
working "hard coded" software needs thorough analysis and understanding of the
given problem. In layman's terms, we simply seek to make Duckietown a safer
place.Comment: Project report and analysis for the Duckietown Project
(https://www.duckietown.org/). 17 pages and 38 figure
The AI Driving Olympics at NeurIPS 2018
Despite recent breakthroughs, the ability of deep learning and reinforcement
learning to outperform traditional approaches to control physically embodied
robotic agents remains largely unproven. To help bridge this gap, we created
the 'AI Driving Olympics' (AI-DO), a competition with the objective of
evaluating the state of the art in machine learning and artificial intelligence
for mobile robotics. Based on the simple and well specified autonomous driving
and navigation environment called 'Duckietown', AI-DO includes a series of
tasks of increasing complexity -- from simple lane-following to fleet
management. For each task, we provide tools for competitors to use in the form
of simulators, logs, code templates, baseline implementations and low-cost
access to robotic hardware. We evaluate submissions in simulation online, on
standardized hardware environments, and finally at the competition event. The
first AI-DO, AI-DO 1, occurred at the Neural Information Processing Systems
(NeurIPS) conference in December 2018. The results of AI-DO 1 highlight the
need for better benchmarks, which are lacking in robotics, as well as improved
mechanisms to bridge the gap between simulation and reality.Comment: Competition, robotics, safety-critical AI, self-driving cars,
autonomous mobility on demand, Duckietow
Adaptive Beaconing Approaches for Vehicular ad hoc Networks: A Survey
Vehicular communication requires vehicles to self-organize through the
exchange of periodic beacons. Recent analysis on beaconing indicates that the
standards for beaconing restrict the desired performance of vehicular
applications. This situation can be attributed to the quality of the available
transmission medium, persistent change in the traffic situation and the
inability of standards to cope with application requirements. To this end, this
paper is motivated by the classifications and capability evaluations of
existing adaptive beaconing approaches. To begin with, we explore the anatomy
and the performance requirements of beaconing. Then, the beaconing design is
analyzed to introduce a design-based beaconing taxonomy. A survey of the
state-of-the-art is conducted with an emphasis on the salient features of the
beaconing approaches. We also evaluate the capabilities of beaconing approaches
using several key parameters. A comparison among beaconing approaches is
presented, which is based on the architectural and implementation
characteristics. The paper concludes by discussing open challenges in the
field
An End-to-End System for Crowdsourced 3d Maps for Autonomous Vehicles: The Mapping Component
Autonomous vehicles rely on precise high definition (HD) 3d maps for
navigation. This paper presents the mapping component of an end-to-end system
for crowdsourcing precise 3d maps with semantically meaningful landmarks such
as traffic signs (6 dof pose, shape and size) and traffic lanes (3d splines).
The system uses consumer grade parts, and in particular, relies on a single
front facing camera and a consumer grade GPS. Using real-time sign and lane
triangulation on-device in the vehicle, with offline sign/lane clustering
across multiple journeys and offline Bundle Adjustment across multiple journeys
in the backend, we construct maps with mean absolute accuracy at sign corners
of less than 20 cm from 25 journeys. To the best of our knowledge, this is the
first end-to-end HD mapping pipeline in global coordinates in the automotive
context using cost effective sensors
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