29,133 research outputs found
Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application
While the development of Vehicle-to-Vehicle (V2V) safety applications based
on Dedicated Short-Range Communications (DSRC) has been extensively undergoing
standardization for more than a decade, such applications are extremely missing
for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between
VRUs and vehicles was the main reason for this lack of attention. Recent
developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this
perspective. Leveraging the existing V2V platforms, we propose a new framework
using a DSRC-enabled smartphone to extend safety benefits to VRUs. The
interoperability of applications between vehicles and portable DSRC enabled
devices is achieved through the SAE J2735 Personal Safety Message (PSM).
However, considering the fact that VRU movement dynamics, response times, and
crash scenarios are fundamentally different from vehicles, a specific framework
should be designed for VRU safety applications to study their performance. In
this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P)
framework to provide situational awareness and hazard detection based on the
most common and injury-prone crash scenarios. The details of our VRU safety
module, including target classification and collision detection algorithms, are
explained next. Furthermore, we propose and evaluate a mitigating solution for
congestion and power consumption issues in such systems. Finally, the whole
system is implemented and analyzed for realistic crash scenarios
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
Update Delay: A new Information-Centric Metric for a Combined Communication and Application Level Reliability Evaluation of CAM based Safety Applications
Standard network metrics, such as throughput, latency and reception probability, are the most popular performance indicators used in the literature to describe and compare communication protocol variations. However, these “traditional” network-centric PI are not adapted to the distributed, information-centric nature of the beaconing communication pattern, nor do they cover application level reliability or freshness of information.
In this paper, we introduce a more suitable metric called Update Delay, represented as a Complementary Cumulative Distribution Function (CCDF). We will show how this single Update Delay performance indicator can be an optimal representation of the freshness and reliability of the information about a certain transmitter, i.e. awareness about vehicles and their current state in the vicinity. This paper extends on the methodological aspects of the approach, as well as introduces several concrete examples
Evaluation of estimation approaches on the quality and robustness of collision warning system
Vehicle safety is one of the most challenging aspect of future-generation
autonomous and semi-autonomous vehicles. Collision warning systems (CCWs), as a
proposed solution framework, can be relied as the main structure to address the
issues in this area. In this framework, information plays a very important
role. Each vehicle has access to its own information immediately. However,
another vehicle information is available through a wireless communication. Data
loss is very common issue for such communication approach. As a consequence,
CCW would suffer from providing late or false detection awareness. Robust
estimation of lost data is of this paper interest which its goal is to
reconstruct or estimate lost network data from previous available or estimated
data as close to actual values as possible under different rate of lost. In
this paper, we will investigate and evaluate three different algorithms
including constant velocity, constant acceleration and Kalman estimator for
this purpose. We make a comparison between their performance which reveals the
ability of them in term of accuracy and robustness for estimation and
prediction based on previous samples which at the end affects the quality of
CCW in awareness generation
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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