5 research outputs found
Customized Co-Simulation Environment for Autonomous Driving Algorithm Development and Evaluation
Increasing the implemented SAE level of autonomy in road vehicles requires
extensive simulations and verifications in a realistic simulation environment
before proving ground and public road testing. The level of detail in the
simulation environment helps ensure the safety of a real-world implementation
and reduces algorithm development cost by allowing developers to complete most
of the validation in the simulation environment. Considering sensors like
camera, LIDAR, radar, and V2X used in autonomous vehicles, it is essential to
create a simulation environment that can provide these sensor simulations as
realistically as possible. While sensor simulations are of crucial importance
for perception algorithm development, the simulation environment will be
incomplete for the simulation of holistic AV operation without being
complemented by a realistic vehicle dynamic model and traffic cosimulation.
Therefore, this paper investigates existing simulation environments, identifies
use case scenarios, and creates a cosimulation environment to satisfy the
simulation requirements for autonomous driving function development using the
Carla simulator based on the Unreal game engine for the environment, Sumo or
Vissim for traffic co-simulation, Carsim or Matlab, Simulink for vehicle
dynamics co-simulation and Autoware or the author or user routines for
autonomous driving algorithm co-simulation. As a result of this work, a
model-based vehicle dynamics simulation with realistic sensor simulation and
traffic simulation is presented. A sensor fusion methodology is implemented in
the created simulation environment as a use case scenario. The results of this
work will be a valuable resource for researchers who need a comprehensive
co-simulation environment to develop connected and autonomous driving
algorithms
Hardware-in-the-Loop and Road Testing of RLVW and GLOSA Connected Vehicle Applications
This paper presents an evaluation of two different Vehicle to Infrastructure
(V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light
Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and
use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the
HIL testing to road testing using an experimental connected vehicle. The HIL
simulator used in the testing is a state-of-the-art simulator that consists of
the same hardware like the road side unit and traffic cabinet as is used in
real intersections and allows testing of numerous different traffic and
intersection geometry and timing scenarios realistically. First, the RLVW V2I
algorithm is tested in the HIL simulator and then implemented in an
On-Board-Unit (OBU) in our experimental vehicle and tested at real world
intersections. This same approach of HIL testing followed by testing in real
intersections using our experimental vehicle is later extended to the GLOSA
application. The GLOSA application that is tested in this paper has both an
optimal speed advisory for passing at the green light and also includes a red
light violation warning system. The paper presents the HIL and experimental
vehicle evaluation systems, information about RLVW and GLOSA and HIL simulation
and road testing results and their interpretations
Virtual and Real Data Populated Intersection Visualization and Testing Tool for V2X Application Development
The capability afforded by Vehicle-to-Vehicle communication improves
situational awareness and provides advantages for many of the traffic problems
caused by reduced visibility or No-Line-of-Sight situations, being useful for
both autonomous and non-autonomous driving. Additionally, with the traffic
light Signal Phase and Timing and Map Datainformation and other advisory
information provided with Vehicle-to-Infrastructure (V2I) communication,
outcomes which benefit the driver in the long run, such as reducing fuel
consumption with speed regulation or decreasing traffic congestion through
optimal speed advisories, providing red light violation warning messages and
intersection motion assist messages for collision-free intersection maneuvering
are all made possible. However, developing applications to obtain these
benefits requires an intensive development process within a lengthy testing
period. Understanding the intersection better is a large part of this
development process. Being able to see what information is broadcasted and how
this information translates into the real world would both benefit the
development of these highly useful applications and also ensure faster
evaluation, when presented visually, using an easy to use and interactive tool.
Moreover, recordings of this broadcasted information can be modified and used
for repeated testing. Modification of the data makes it flexible and allows us
to use it for a variety of testing scenarios at a virtually populated
intersection. Based on this premise, this paper presents and demonstrates
visualization tools to project SPaT, MAP and Basic Safety Message information
into easy to read real-world based graphs. Also, it provides information about
the modification of the real-world data to allow creation of a virtually
populated intersection, along with the capability to also inject virtual
vehicles at this intersection
Mobile Safety Application for Pedestrians
Vulnerable Road User (VRU) safety has been an important issue throughout the
years as corresponding fatality numbers in traffic have been increasing each
year. With the developments in connected vehicle technology, there are new and
easier ways of implementing Vehicle to Everything (V2X) communication which can
be utilized to provide safety and early warning benefits for VRUs. Mobile
phones are one important point of interest with their sensors being increased
in quantity and quality and improved in terms of accuracy. Bluetooth and
extended Bluetooth technology in mobile phones has enhanced support to carry
larger chunks of information to longer distances. The work we discuss in this
paper is related to a mobile application that utilizes the mobile phone sensors
and Bluetooth communication to implement Personal Safety Message (PSM)
broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V)
based safety warning structure. This implementation allows the drivers to
receive a warning on their mobile phones and be more careful about the
pedestrian intending to cross the street. As a result, the driver has much more
time to safely slow down and stop at the intersection. Most importantly, thanks
to the wireless nature of Bluetooth connection and long-range mode in Bluetooth
5.0, most dangerous cases such as reduced visibility or No-Line-of-Sight (NLOS)
conditions can be remedied
V2X Communication between Connected and Automated Vehicles (CAVs) and Unmanned Aerial Vehicles (UAVs)
Connectivity between ground vehicles can be utilized and expanded to include aerial vehicles for coordinated missions. Using Vehicle-to-Everything (V2X) communication technologies, a communication link can be established between Connected and Autonomous vehicles (CAVs) and Unmanned Aerial vehicles (UAVs). Hardware implementation and testing of a ground-to-air communication link are crucial for real-life applications. In this paper, the V2X communication and coordinated mission of a CAV & UAV are presented. Four methods were utilized to establish communication between the hardware and software components, namely Dedicated Short Range communication (DSRC), User Datagram Protocol (UDP), 4G internet-based WebSocket and Transmission Control Protocol (TCP). These communication links were used together for a real-life use case scenario called Quick Clear demonstration. In this scenario, the first aim was to send the accident location information from the CAV to the UAV through DSRC communication. On the UAV side, the wired connection between the DSRC modem and Raspberry Pi companion computer was established through UDP to get the accident location from CAV to the companion computer. Raspberry Pi first connected to a traffic contingency management system (CMP) through TCP to send CAV and UAV location, as well as the accident location, information to the CMP. Raspberry Pi also utilized WebSocket communication to connect to a web server to send photos that were taken by the camera that was mounted on the UAV. The Quick Clear demonstration scenario was tested for both a stationary test and dynamic flight cases. The latency results show satisfactory performance in the data transfer speed between test components with UDP having the least latency. The package drop percentage analysis shows that the DSRC communication showed the best performance among the four methods studied here. All in all, the outcome of this experimentation study shows that this communication structure can be utilized for real-life scenarios for successful implementation