81,577 research outputs found
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
Scenario-based testing for the safety validation of highly automated vehicles
is a promising approach that is being examined in research and industry. This
approach heavily relies on data from real-world scenarios to derive the
necessary scenario information for testing. Measurement data should be
collected at a reasonable effort, contain naturalistic behavior of road users
and include all data relevant for a description of the identified scenarios in
sufficient quality. However, the current measurement methods fail to meet at
least one of the requirements. Thus, we propose a novel method to measure data
from an aerial perspective for scenario-based validation fulfilling the
mentioned requirements. Furthermore, we provide a large-scale naturalistic
vehicle trajectory dataset from German highways called highD. We evaluate the
data in terms of quantity, variety and contained scenarios. Our dataset
consists of 16.5 hours of measurements from six locations with 110 000
vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane
changes. The highD dataset is available online at: http://www.highD-dataset.comComment: IEEE International Conference on Intelligent Transportation Systems
(ITSC) 201
Traffic Danger Recognition With Surveillance Cameras Without Training Data
We propose a traffic danger recognition model that works with arbitrary
traffic surveillance cameras to identify and predict car crashes. There are too
many cameras to monitor manually. Therefore, we developed a model to predict
and identify car crashes from surveillance cameras based on a 3D reconstruction
of the road plane and prediction of trajectories. For normal traffic, it
supports real-time proactive safety checks of speeds and distances between
vehicles to provide insights about possible high-risk areas. We achieve good
prediction and recognition of car crashes without using any labeled training
data of crashes. Experiments on the BrnoCompSpeed dataset show that our model
can accurately monitor the road, with mean errors of 1.80% for distance
measurement, 2.77 km/h for speed measurement, 0.24 m for car position
prediction, and 2.53 km/h for speed prediction.Comment: To be published in proceedings of Advanced Video and Signal-based
Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 378-383,
IEE
An Intelligent Monitoring System of Vehicles on Highway Traffic
Vehicle speed monitoring and management of highways is the critical problem
of the road in this modern age of growing technology and population. A poor
management results in frequent traffic jam, traffic rules violation and fatal
road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to
address this problem is time-consuming, expensive and tedious. This paper
presents an efficient framework to produce a simple, cost efficient and
intelligent system for vehicle speed monitoring. The proposed method uses an HD
(High Definition) camera mounted on the road side either on a pole or on a
traffic signal for recording video frames. On the basis of these frames, a
vehicle can be tracked by using radius growing method, and its speed can be
calculated by calculating vehicle mask and its displacement in consecutive
frames. The method uses pattern recognition, digital image processing and
mathematical techniques for vehicle detection, tracking and speed calculation.
The validity of the proposed model is proved by testing it on different
highways.Comment: 5 page
The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents
In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
FlightGoggles is a photorealistic sensor simulator for perception-driven
robotic vehicles. The key contributions of FlightGoggles are twofold. First,
FlightGoggles provides photorealistic exteroceptive sensor simulation using
graphics assets generated with photogrammetry. Second, it provides the ability
to combine (i) synthetic exteroceptive measurements generated in silico in real
time and (ii) vehicle dynamics and proprioceptive measurements generated in
motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of
simulating a virtual-reality environment around autonomous vehicle(s). While a
vehicle is in flight in the FlightGoggles virtual reality environment,
exteroceptive sensors are rendered synthetically in real time while all complex
extrinsic dynamics are generated organically through the natural interactions
of the vehicle. The FlightGoggles framework allows for researchers to
accelerate development by circumventing the need to estimate complex and
hard-to-model interactions such as aerodynamics, motor mechanics, battery
electrochemistry, and behavior of other agents. The ability to perform
vehicle-in-the-loop experiments with photorealistic exteroceptive sensor
simulation facilitates novel research directions involving, e.g., fast and
agile autonomous flight in obstacle-rich environments, safe human interaction,
and flexible sensor selection. FlightGoggles has been utilized as the main test
for selecting nine teams that will advance in the AlphaPilot autonomous drone
racing challenge. We survey approaches and results from the top AlphaPilot
teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be
found at https://flightgoggles.mit.edu. Revision includes description of new
FlightGoggles features, such as a photogrammetric model of the MIT Stata
Center, new rendering settings, and a Python AP
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