4,431 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
Pre-Deployment Testing of Low Speed, Urban Road Autonomous Driving in a Simulated Environment
Low speed autonomous shuttles emulating SAE Level L4 automated driving using
human driver assisted autonomy have been operating in geo-fenced areas in
several cities in the US and the rest of the world. These autonomous vehicles
(AV) are operated by small to mid-sized technology companies that do not have
the resources of automotive OEMs for carrying out exhaustive, comprehensive
testing of their AV technology solutions before public road deployment. Due to
the low speed of operation and hence not operating on roads containing
highways, the base vehicles of these AV shuttles are not required to go through
rigorous certification tests. The way the driver assisted AV technology is
tested and allowed for public road deployment is continuously evolving but is
not standardized and shows differences between the different states where these
vehicles operate. Currently, AVs and AV shuttles deployed on public roads are
using these deployments for testing and improving their technology. However,
this is not the right approach. Safe and extensive testing in a lab and
controlled test environment including Model-in-the-Loop (MiL),
Hardware-in-the-Loop (HiL) and Autonomous-Vehicle-in-the-Loop (AViL) testing
should be the prerequisite to such public road deployments. This paper presents
three dimensional virtual modeling of an AV shuttle deployment site and
simulation testing in this virtual environment. We have two deployment sites in
Columbus of these AV shuttles through the Department of Transportation funded
Smart City Challenge project named Smart Columbus. The Linden residential area
AV shuttle deployment site of Smart Columbus is used as the specific example
for illustrating the AV testing method proposed in this paper
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
Cooperative Collision Avoidance in a Connected Vehicle Environment
Connected vehicle (CV) technology is among the most heavily researched areas
in both the academia and industry. The vehicle to vehicle (V2V), vehicle to
infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities
enable critical situational awareness. In some cases, these vehicle
communication safety capabilities can overcome the shortcomings of other sensor
safety capabilities because of external conditions such as 'No Line of Sight'
(NLOS) or very harsh weather conditions. Connected vehicles will help cities
and states reduce traffic congestion, improve fuel efficiency and improve the
safety of the vehicles and pedestrians. On the road, cars will be able to
communicate with one another, automatically transmitting data such as speed,
position, and direction, and send alerts to each other if a crash seems
imminent. The main focus of this paper is the implementation of Cooperative
Collision Avoidance (CCA) for connected vehicles. It leverages the Vehicle to
Everything (V2X) communication technology to create a real-time implementable
collision avoidance algorithm along with decision-making for a vehicle that
communicates with other vehicles. Four distinct collision risk environments are
simulated on a cost effective Connected Autonomous Vehicle (CAV) Hardware in
the Loop (HIL) simulator to test the overall algorithm in real-time with real
electronic control and communication hardware
Dynamic Speed Harmonization
In the last decade, the accelerated advancements in manufacturing techniques
and material science enabled the automotive industry to manufacture commercial
vehicles at more affordable rates. This, however, brought about roadways having
to accommodate an ever-increasing number of vehicles every day. However, some
roadways, during specific hours of the day, had already been on the brink of
reaching their capacity to withstand the number of vehicles travelling on them.
Hence, overcrowded roadways create slow traffic, and sometimes, bottlenecks. In
this paper, a Dynamic Speed Harmonization (DSH) algorithm that regulates the
speed of a vehicle to prevent it from being affected by bottlenecks has been
presented. First, co-simulations were run between MATLAB Simulink and CarSim to
test different deceleration profiles. Then, Hardware-in-the-Loop (HIL)
simulations were run with a Road Side Unit (RSU), which emulated a roadside
detector that spotted bottlenecks and sent information to the Connected Vehicle
about the position of the queue and the average speed of the vehicles at the
queue. The DSH algorithm was also tested on a track to compare the performance
of the different deceleration profiles in terms of ride comfort.Comment: 7 pages, 5 figure
The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks
Microscopic traffic simulators that simulate realistic traffic flow are
crucial in studying, understanding and evaluating the fuel usage and mobility
effects of having a higher number of autonomous vehicles (AVs) in traffic under
realistic mixed traffic conditions including both autonomous and non-autonomous
vehicles. In this paper, L4-L5 AVs with varying penetration rates in total
traffic flow were simulated using the microscopic traffic simulator Vissim on
urban, mixed and freeway roadways. The roadways used in these simulations were
replicas of real roadways in and around Columbus, Ohio, including an AV shuttle
routes in operation. The road-specific information regarding each roadway, such
as the number of traffic lights and positions, number of STOP signs and
positions, and speed limits, were gathered using OpenStreetMap with SUMO. In
simulating L4-L5 AVs, the All-Knowing CoEXist AV and a vehicle with Wiedemann
74 driver were taken to represent AV and non-AV driving, respectively. Then,
the driving behaviors, such as headway time and car following, desired
acceleration and deceleration profiles of AV, and non-AV car following and lane
change models were modified. The effect of having varying penetration rates of
L4-L5 AVs were then evaluated using criteria such as average fuel consumption,
existence of queues and their average/maximum length, total number of vehicles
in the simulation, average delay experience by all vehicles, total number of
stops experienced by all vehicles, and total emission of CO, NOx and volatile
organic compounds (VOC) from the vehicles in the simulation. The results show
that while increasing penetration rates of L4-L5 AVs generally improve overall
fuel efficiency and mobility of the traffic network, there were also cases when
the opposite trend was observed
Discrete-time Robust PD Controlled System with DOB/CDOB Compensation for High Speed Autonomous Vehicle Path Following
Autonomous vehicle path following performance is one of significant
consideration. This paper presents discrete time design of robust PD controlled
system with disturbance observer (DOB) and communication disturbance observer
(CDOB) compensation to enhance autonomous vehicle path following performance.
Although always implemented on digital devices, DOB and CDOB structure are
usually designed in continuous time in the literature and also in our previous
work. However, it requires high sampling rate for continuous-time design block
diagram to automatically convert to corresponding discrete-time controller
using rapid controller prototyping systems. In this paper, direct discrete time
design is carried out. Digital PD feedback controller is designed based on the
nominal plant using the proposed parameter space approach. Zero order hold
method is applied to discretize the nominal plant, DOB and CDOB structure in
continuous domain. Discrete time DOB is embedded into the steering to path
following error loop for model regulation in the presence of uncertainty in
vehicle parameters such as vehicle mass, vehicle speed and road-tire friction
coefficient and rejecting external disturbance like crosswind force. On the
other hand, time delay from CAN bus based sensor and actuator command
interfaces results in degradation of system performance since large negative
phase angles are added to the plant frequency response. Discrete time CDOB
compensated control system can be used for time delay compensation where the
accurate knowledge of delay time value is not necessary. A validated model of
our lab Ford Fusion hybrid automated driving research vehicle is used for the
simulation analysis while the vehicle is driving at high speed. Simulation
results successfully demonstrate the improvement of autonomous vehicle path
following performance with the proposed discrete time DOB and CDOB structure
An Investigation into the Performance Evaluation of Connected Vehicle Applications: From Real-World Experiment to Parallel Simulation Paradigm
A novel system was developed that provides drivers lane merge advisories, using vehicle trajectories obtained through Dedicated Short Range Communication (DSRC). It was successfully tested on a freeway using three vehicles, then targeted for further testing, via simulation. The failure of contemporary simulators to effectively model large, complex urban transportation networks then motivated further research into distributed and parallel traffic simulation. An architecture for a closed-loop, parallel simulator was devised, using a new algorithm that accounts for boundary nodes, traffic signals, intersections, road lengths, traffic density, and counts of lanes; it partitions a sample, Tennessee road network more efficiently than tools like METIS, which increase interprocess communications (IPC) overhead by partitioning more transportation corridors. The simulator uses logarithmic accumulation to synchronize parallel simulations, further reducing IPC. Analyses suggest this eliminates up to one-third of IPC overhead incurred by a linear accumulation model
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