612 research outputs found
Minimally Disruptive Cooperative Lane-change Maneuvers
A lane-change maneuver on a congested highway could be severely disruptive or
even infeasible without the cooperation of neighboring cars. However,
cooperation with other vehicles does not guarantee that the performed maneuver
will not have a negative impact on traffic flow unless it is explicitly
considered in the cooperative controller design. In this letter, we present a
socially compliant framework for cooperative lane-change maneuvers for an
arbitrary number of CAVs on highways that aims to interrupt traffic flow as
minimally as possible. Moreover, we explicitly impose feasibility constraints
in the optimization formulation by using reachability set theory, leading to a
unified design that removes the need for an iterative procedure used in prior
work. We quantitatively evaluate the effectiveness of our framework and compare
it against previously offered approaches in terms of maneuver time and incurred
throughput disruption.Comment: 6 pages, 2 figure
Control and optimization methods for problems in intelligent transportation systems
This thesis aims to address three research topics in intelligent transportation systems
which include multi-intersection traffic light control based on stochastic flow models with
delays and blocking, optimization of mobility-on-demand systems using event-driven receding
horizon control and the optimal control of lane change maneuvers in highways for
connected and automated vehicles.
First, for the traffic light control work, we extend Stochastic Flow Models (SFMs),
used for a large class of discrete event and hybrid systems, by including the delays which
typically arise in flow movements, as well as blocking effects due to space constraints. We
apply this framework to the multi-intersection traffic light control problem by including
transit delays for vehicles moving from one intersection to the next and possible blocking
between two intersections. Using Infinitesimal Perturbation Analysis (IPA) for this SFM
with delays and possible blocking, we derive new on-line gradient estimates of several
congestion cost metrics with respect to the controllable green and red cycle lengths. The
IPA estimators are used to iteratively adjust light cycle lengths to improve performance
and, in conjunction with a standard gradient-based algorithm, to obtain optimal values
which adapt to changing traffic conditions.
The second problem relates to developing an event-driven Receding Horizon Control
(RHC) scheme for a Mobility-on-Demand System (MoDS) in a transportation network
where vehicles may be shared to pick up and drop off passengers so as to minimize a
weighted sum of passenger waiting and traveling times. Viewed as a discrete event system,
the event-driven nature of the controller significantly reduces the complexity of the vehicle
assignment problem, thus enabling its real-time implementation.
Finally, optimal control policies are derived for a Connected Automated Vehicle (CAV)
cooperating with neighboring CAVs in order to implement a lane change maneuver consisting
of a longitudinal phase where the CAV properly positions itself relative to the cooperating
neighbors and a lateral phase where it safely changes lanes. For the first phase, the maneuver time subject to safety constraints and subsequently the associated energy consumption of all cooperating vehicles in this maneuver are optimized. For the second phase, time and energy are jointly optimized based on three different solution methods including a real-time approach based on Control Barrier Functions (CBFs). Structural properties of the optimal policies which simplify the solution derivations are provided in the case of the longitudinal maneuver, leading to analytical optimal control expressions. The solutions, when they exist, are guaranteed to satisfy safety constraints for all vehicles involved in the maneuver
Fully automated urban traffic system
The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible
Vehicular Cooperative Maneuvers -- Quo Vaditis?
Vehicles will not only get more and more automated, but they will also
cooperate in new ways. Currently, human-driven vehicles begin to communicate
with each other using vehicle-to-everything technology. Future vehicles will
use communication to share sensor data and even negotiate cooperative
maneuvers. This lets them learn more about the environment and improves traffic
flow and passenger comfort as more predictable maneuvers are likely to lead to
a smoother ride. This paper introduces the most important concepts around
cooperative vehicular maneuvers. We also summarize currently open challenges
and questions to answer before a deployment can begin. Afterward, we give some
perspectives on the further evolution of cooperative maneuvers and beyond.Comment: 8 pages incl. references and author biographies, 4 figures incl.
multiple sub-figure
Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
Modern driver assistance systems as well as autonomous vehicles take their
decisions based on local maps of the environment. These maps include, for
example, surrounding moving objects perceived by sensors as well as routes and
navigation information. Current research in the field of environment mapping is
concerned with two major challenges. The first one is the integration of
information from different sources e.g. on-board sensors like radar, camera,
ultrasound and lidar, offline map data or backend information. The second
challenge comprises in finding an abstract representation of this aggregated
information with suitable interfaces for different driving functions and
traffic situations. To overcome these challenges, an extended environment model
is a reasonable choice. In this paper, we show that role-based motion
predictions in combination with v2x-extended environment models are able to
contribute to increased traffic safety and driving comfort. Thus, we combine
the mentioned research areas and show possible improvements, using the example
of a threading process at a motorway access road. Furthermore, it is shown that
already an average v2x equipment penetration of 80% can lead to a significant
improvement of 0.33m/s^2 of the total acceleration and 12m more safety distance
compared to non v2x-equipped vehicles during the threading process.Comment: the article has been accepted for publication during the 16th IEEE
International Conference on Intelligent Computer Communication and Processing
(ICCP 2020), 8 pages, 8 figures, 1 tabl
Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects
Connected and automated vehicles (CAVs) have emerged as a potential solution
to the future challenges of developing safe, efficient, and eco-friendly
transportation systems. However, CAV control presents significant challenges,
given the complexity of interconnectivity and coordination required among the
vehicles. To address this, multi-agent reinforcement learning (MARL), with its
notable advancements in addressing complex problems in autonomous driving,
robotics, and human-vehicle interaction, has emerged as a promising tool for
enhancing the capabilities of CAVs. However, there is a notable absence of
current reviews on the state-of-the-art MARL algorithms in the context of CAVs.
Therefore, this paper delivers a comprehensive review of the application of
MARL techniques within the field of CAV control. The paper begins by
introducing MARL, followed by a detailed explanation of its unique advantages
in addressing complex mobility and traffic scenarios that involve multiple
agents. It then presents a comprehensive survey of MARL applications on the
extent of control dimensions for CAVs, covering critical and typical scenarios
such as platooning control, lane-changing, and unsignalized intersections. In
addition, the paper provides a comprehensive review of the prominent simulation
platforms used to create reliable environments for training in MARL. Lastly,
the paper examines the current challenges associated with deploying MARL within
CAV control and outlines potential solutions that can effectively overcome
these issues. Through this review, the study highlights the tremendous
potential of MARL to enhance the performance and collaboration of CAV control
in terms of safety, travel efficiency, and economy
From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation
Context: Competitions for self-driving cars facilitated the development and
research in the domain of autonomous vehicles towards potential solutions for
the future mobility.
Objective: Miniature vehicles can bridge the gap between simulation-based
evaluations of algorithms relying on simplified models, and those
time-consuming vehicle tests on real-scale proving grounds.
Method: This article combines findings from a systematic literature review,
an in-depth analysis of results and technical concepts from contestants in a
competition for self-driving miniature cars, and experiences of participating
in the 2013 competition for self-driving cars.
Results: A simulation-based development platform for real-scale vehicles has
been adapted to support the development of a self-driving miniature car.
Furthermore, a standardized platform was designed and realized to enable
research and experiments in the context of future mobility solutions.
Conclusion: A clear separation between algorithm conceptualization and
validation in a model-based simulation environment enabled efficient and
riskless experiments and validation. The design of a reusable, low-cost, and
energy-efficient hardware architecture utilizing a standardized
software/hardware interface enables experiments, which would otherwise require
resources like a large real-scale test track.Comment: 17 pages, 19 figues, 2 table
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