21 research outputs found
The Limited Integrator Model Regulator And its Use in Vehicle Steering Control
Unexpected yaw disturbances like braking on unilaterally icy road, side wind
forces and tire rupture are very difficult to handle by the driver of a road
vehicle, due to his/her large panic reaction period ranging between 0.5 to 2
seconds. Automatic driver assist systems provide counteracting yaw moments
during this driver panic reaction period to maintain the stability of the yaw
dynamics of the vehicle. An active steering based driver assist system that
uses the model regulator control architecture is introduced and used here for
yaw dynamics stabilization in such situations. The model regulator which is a
special form of a two degree of freedom control architecture is introduced and
explained in detail in a tutorial fashion whereby its integral action
capability, among others, is also shown. An auxiliary steering actuation system
is assumed and a limited integrator version of the model regulator based
steering controller is developed in order not to saturate the auxiliary
steering actuator. This low frequency limited integrator implementation also
allows the driver to take care of low frequency steering and disturbance
rejection tasks. Linear simulation results are used to demonstrate the
effectiveness of the proposed method
Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method
This paper presents the real time implementation of socially acceptable collision avoidance using the elastic band method for low speed autonomous shuttles operating in high pedestrian density environments. The modeling and validation of the research autonomous vehicle used in the experimental implementation is presented first, followed by the details of the Hardware-In-the-Loop connected and autonomous vehicle simulator used. The socially acceptable collision avoidance algorithm is formulated using the elastic band method as an online, local path modification algorithm. Parameter space based robust feedback plus feedforward steering controller design is used. Model-in-the-loop, Hardware-In-the-Loop and road testing in a proving ground are used to demonstrate the effectiveness of the real time implementation of the elastic band based socially acceptable collision avoidance method of this paper
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
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
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
Feasibility of Local Trajectory Planning for Level-2+ Semi-autonomous Driving without Absolute Localization
Autonomous driving has long grappled with the need for precise absolute
localization, making full autonomy elusive and raising the capital entry
barriers for startups. This study delves into the feasibility of local
trajectory planning for level-2+ (L2+) semi-autonomous vehicles without the
dependence on accurate absolute localization. Instead, we emphasize the
estimation of the pose change between consecutive planning frames from motion
sensors and integration of relative locations of traffic objects to the local
planning problem under the ego car's local coordinate system, therefore
eliminating the need for an absolute localization. Without the availability of
absolute localization for correction, the measurement errors of speed and yaw
rate greatly affect the estimation accuracy of the relative pose change between
frames. We proved that the feasibility/stability of the continuous planning
problem under such motion sensor errors can be guaranteed at certain defined
conditions. This was achieved by formulating it as a Lyapunov-stability
analysis problem. Moreover, a simulation pipeline was developed to further
validate the proposed local planning method. Simulations were conducted at two
traffic scenes with different error settings for speed and yaw rate
measurements. The results substantiate the proposed framework's functionality
even under relatively inferior sensor errors. We also experiment the stability
limits of the planned results under abnormally larger motion sensor errors. The
results provide a good match to the previous theoretical analysis. Our findings
suggested that precise absolute localization may not be the sole path to
achieving reliable trajectory planning, eliminating the necessity for
high-accuracy dual-antenna GPS as well as the high-fidelity maps for SLAM
localization.Comment: 11 pages, 13 figures, github url:
https://github.com/codezs09/l2_frenet_planne
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
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
Autonomous Vehicle Decision-Making with Policy Prediction for Handling a Round Intersection
Autonomous shuttles have been used as end-mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of autonomous shuttles. Round intersections are more challenging because it is more difficult to perceive the other vehicles in and near the intersection. Thus, this paper focuses on the decision-making of autonomous vehicles for handling round intersections. The round intersection is introduced first, followed by introductions of the Markov Decision Process (MDP), the Partially Observable Markov Decision Process (POMDP) and the Object-Oriented Partially Observable Markov Decision Process (OOPOMDP), which are used for decision-making with uncertain knowledge of the motion of the other vehicles. The Partially Observable Monte-Carlo Planning (POMCP) algorithm is used as the solution method and OOPOMDP is applied to the decision-making of autonomous vehicles in round intersections. Decision-making is formulated first as a POMDP problem, and the penalty function is formulated and set accordingly. This is followed by an improvement in decision-making with policy prediction. Augmented objective state and policy-based state transition are introduced, and simulations are used to demonstrate the effectiveness of the proposed method for collision-free handling of round intersections by the ego vehicle