6,103 research outputs found
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
A mosaic of eyes
Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties
Towards Secure and Safe Appified Automated Vehicles
The advancement in Autonomous Vehicles (AVs) has created an enormous market
for the development of self-driving functionalities,raising the question of how
it will transform the traditional vehicle development process. One adventurous
proposal is to open the AV platform to third-party developers, so that AV
functionalities can be developed in a crowd-sourcing way, which could provide
tangible benefits to both automakers and end users. Some pioneering companies
in the automotive industry have made the move to open the platform so that
developers are allowed to test their code on the road. Such openness, however,
brings serious security and safety issues by allowing untrusted code to run on
the vehicle. In this paper, we introduce the concept of an Appified AV platform
that opens the development framework to third-party developers. To further
address the safety challenges, we propose an enhanced appified AV design schema
called AVGuard, which focuses primarily on mitigating the threats brought about
by untrusted code, leveraging theory in the vehicle evaluation field, and
conducting program analysis techniques in the cybersecurity area. Our study
provides guidelines and suggested practice for the future design of open AV
platforms
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