1,105 research outputs found
Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling
In this paper, we design a supervisor to prevent vehicle collisions at
intersections. An intersection is modeled as an area containing multiple
conflict points where vehicle paths cross in the future. At every time step,
the supervisor determines whether there will be more than one vehicle in the
vicinity of a conflict point at the same time. If there is, then an impending
collision is detected, and the supervisor overrides the drivers to avoid
collision. A major challenge in the design of a supervisor as opposed to an
autonomous vehicle controller is to verify whether future collisions will occur
based on the current drivers choices. This verification problem is particularly
hard due to the large number of vehicles often involved in intersection
collision, to the multitude of conflict points, and to the vehicles dynamics.
In order to solve the verification problem, we translate the problem to a
job-shop scheduling problem that yields equivalent answers. The job-shop
scheduling problem can, in turn, be transformed into a mixed-integer linear
program when the vehicle dynamics are first-order dynamics, and can thus be
solved by using a commercial solver.Comment: Submitted to Hybrid Systems: Computation and Control (HSCC) 201
An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections
In this paper, we provide a decentralized optimal control framework for
coordinating connected and automated vehicles (CAVs) in two interconnected
intersections. We formulate a control problem and provide a solution that can
be implemented in real time. The solution yields the optimal
acceleration/deceleration of each CAV under the safety constraint at "conflict
zones," where there is a chance of potential collision. Our objective is to
minimize travel time for each CAV. If no such solution exists, then each CAV
solves an energy-optimal control problem. We evaluate the effectiveness of the
efficiency of the proposed framework through simulation.Comment: 8 pages, 5 figures, IEEE CONFERENCE ON CONTROL TECHNOLOGY AND
APPLICATIONS 201
Least Restrictive and Minimally Deviating Supervisor for Safe Semi-Autonomous Driving at an Intersection: An MIQP Approach
International audienceAlthough significant progress has been made in the last few years towards cooperative and autonomous driving, the transition from human-driven to fully automated vehicles is expected to happen slowly. The question of semi-autonomous driving, where Advanced Driver Assistance Systems assist human drivers in their decisions, will therefore become increasingly important. In this paper, we consider the problem of safe intersection crossing for semi-autonomous vehicles with communication capacities. We design an intersection supervisor based on a mixed-integer quadratic programming approach which monitors the control inputs of each vehicle, and overrides those controls when necessary to ensure that all vehicles can navigate safely. Moreover, the solution control deviates minimally from the vehicles target inputs: overriding only occurs when it is strictly necessary, in which case the control is chosen as close as possible to the driver's intent. We theoretically prove that the supervisor needs only consider a finite future time horizon to ensure safety and deadlock avoidance over an infinite time horizon, and we demonstrate through simulation that this algorithm can work in real time. Additionally, unlike previous work, our formulation is suitable for complex intersection geometries with a high number of vehicles
A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles
Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy
Simulation in Automated Guided Vehicle System Design
The intense global competition that manufacturing companies face today results in an
increase of product variety and shorter product life cycles. One response to this threat is
agile manufacturing concepts. This requires materials handling systems that are agile
and capable of reconfiguration. As competition in the world marketplace becomes
increasingly customer-driven, manufacturing environments must be highly
reconfigurable and responsive to accommodate product and process changes, with rigid,
static automation systems giving way to more flexible types.
Automated Guided Vehicle Systems (AGVS) have such capabilities and AGV
functionality has been developed to improve flexibility and diminish the traditional
disadvantages of AGV-systems. The AGV-system design is however a multi-faceted
problem with a large number of design factors of which many are correlating and
interdependent. Available methods and techniques exhibit problems in supporting the
whole design process. A research review of the work reported on AGVS development in
combination with simulation revealed that of 39 papers only four were industrially
related. Most work was on the conceptual design phase, but little has been reported on
the detailed simulation of AGVS.
Semi-autonomous vehicles (SA V) are an innovative concept to overcome the problems
of inflexible -systems and to improve materials handling functionality. The SA V
concept introduces a higher degree of autonomy in industrial AGV -systems with the
man-in-the-Ioop. The introduction of autonomy in industrial applications is approached
by explicitly controlling the level of autonomy at different occasions. The SA V s are
easy to program and easily reconfigurable regarding navigation systems and material
handling equipment. Novel approaches to materials handling like the SA V -concept
place new requirements on the AGVS development and the use of simulation as a part
of the process. Traditional AGV -system simulation approaches do not fully meet these
requirements and the improved functionality of AGVs is not used to its full power.
There is a considerflble potential in shortening the AGV -system design-cycle, and thus
the manufacturing system design-cycle, and still achieve more accurate solutions well
suited for MRS tasks.
Recent developments in simulation tools for manufacturing have improved production
engineering development and the tools are being adopted more widely in industry. For
the development of AGV -systems this has not fully been exploited. Previous research
has focused on the conceptual part of the design process and many simulation
approaches to AGV -system design lack in validity. In this thesis a methodology is
proposed for the structured development of AGV -systems using simulation. Elements of
this methodology address the development of novel functionality.
The objective of the first research case of this research study was to identify factors for
industrial AGV -system simulation. The second research case focuses on simulation in
the design of Semi-autonomous vehicles, and the third case evaluates a simulation based
design framework. This research study has advanced development by offering a
framework for developing testing and evaluating AGV -systems, based on concurrent
development using a virtual environment. The ability to exploit unique or novel features
of AGVs based on a virtual environment improves the potential of AGV-systems
considerably.University of Skovde. European Commission for funding the INCO/COPERNICUS Projec
Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection
In transportation networks, where traffic lights have traditionally been used
for vehicle coordination, intersections act as natural bottlenecks. A
formidable challenge for existing automated intersections lies in detecting and
reasoning about uncertainty from the operating environment and human-driven
vehicles. In this paper, we propose a risk-aware intelligent intersection
system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs). We
cast the problem as a novel class of Multi-agent Chance-Constrained Stochastic
Shortest Path (MCC-SSP) problems and devise an exact Integer Linear Programming
(ILP) formulation that is scalable in the number of agents' interaction points
(e.g., potential collision points at the intersection). In particular, when the
number of agents within an interaction point is small, which is often the case
in intersections, the ILP has a polynomial number of variables and constraints.
To further improve the running time performance, we show that the collision
risk computation can be performed offline. Additionally, a trajectory
optimization workflow is provided to generate risk-aware trajectories for any
given intersection. The proposed framework is implemented in CARLA simulator
and evaluated under a fully autonomous intersection with AVs only as well as in
a hybrid setup with a signalized intersection for HVs and an intelligent scheme
for AVs. As verified via simulations, the featured approach improves
intersection's efficiency by up to while also conforming to the
specified tunable risk threshold
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