56,465 research outputs found
Processor-in-the-loop architecture design and experimental validation for an autonomous racing vehicle
Self-driving vehicles have experienced an increase in research interest in the last decades. Nevertheless, fully autonomous vehicles are still far from being a common means of transport. This paper presents the design and experimental validation of a processor-in-the-loop (PIL) architecture for an autonomous sports car. The considered vehicle is an all-wheel drive full-electric single-seater prototype. The retained PIL architecture includes all the modules required for autonomous driving at system level: environment perception, trajectory planning, and control. Specifically, the perception pipeline exploits obstacle detection algorithms based on Artificial Intelligence (AI), and the trajectory planning is based on a modified Rapidly-exploring Random Tree (RRT) algorithm based on Dubins curves, while the vehicle is controlled via a Model Predictive Control (MPC) strategy. The considered PIL layout is implemented firstly using a low-cost card-sized computer for fast code verification purposes. Furthermore, the proposed PIL architecture is compared in terms of performance to an alternative PIL using high-performance real-time target computing machine. Both PIL architectures exploit User Datagram Protocol (UDP) protocol to properly communicate with a personal computer. The latter PIL architecture is validated in real-time using experimental data. Moreover, they are also validated with respect to the general autonomous pipeline that runs in parallel on the personal computer during numerical simulation
A novel distributed architecture for UAV indoor navigation
Abstract In the last decade, different indoor flight navigation systems for small Unmanned Aerial Vehicles (UAVs) have been investigated, with a special focus on different configurations and on sensor technologies. The main idea of this paper is to propose a distributed Guidance Navigation and Control (GNC) system architecture, based on Robotic Operation System (ROS) for light weight UAV autonomous indoor flight. The proposed framework is shown to be more robust and flexible than common configurations. A flight controller and companion computer running ROS for control and navigation are also included in the section. Both hardware and software diagrams are given to show the complete architecture. Further works will be based on the experimental validation of the proposed configuration by indoor flight tests
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
A City-Scale ITS-G5 Network for Next-Generation Intelligent Transportation Systems: Design Insights and Challenges
As we move towards autonomous vehicles, a reliable Vehicle-to-Everything
(V2X) communication framework becomes of paramount importance. In this paper we
present the development and the performance evaluation of a real-world
vehicular networking testbed. Our testbed, deployed in the heart of the City of
Bristol, UK, is able to exchange sensor data in a V2X manner. We will describe
the testbed architecture and its operational modes. Then, we will provide some
insight pertaining the firmware operating on the network devices. The system
performance has been evaluated under a series of large-scale field trials,
which have proven how our solution represents a low-cost high-quality framework
for V2X communications. Our system managed to achieve high packet delivery
ratios under different scenarios (urban, rural, highway) and for different
locations around the city. We have also identified the instability of the
packet transmission rate while using single-core devices, and we present some
future directions that will address that.Comment: Accepted for publication to AdHoc-Now 201
An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles
Due to the complexity of the natural world, a programmer cannot foresee all
possible situations, a connected and autonomous vehicle (CAV) will face during
its operation, and hence, CAVs will need to learn to make decisions
autonomously. Due to the sensing of its surroundings and information exchanged
with other vehicles and road infrastructure, a CAV will have access to large
amounts of useful data. While different control algorithms have been proposed
for CAVs, the benefits brought about by connectedness of autonomous vehicles to
other vehicles and to the infrastructure, and its implications on policy
learning has not been investigated in literature. This paper investigates a
data driven driving policy learning framework through an agent-based modelling
approaches. The contributions of the paper are two-fold. A dynamic programming
framework is proposed for in-vehicle policy learning with and without
connectivity to neighboring vehicles. The simulation results indicate that
while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V)
communication of information improves this capability. Furthermore, to overcome
the limitations of sensing in a CAV, the paper proposes a novel concept for
infrastructure-led policy learning and communication with autonomous vehicles.
In infrastructure-led policy learning, road-side infrastructure senses and
captures successful vehicle maneuvers and learns an optimal policy from those
temporal sequences, and when a vehicle approaches the road-side unit, the
policy is communicated to the CAV. Deep-imitation learning methodology is
proposed to develop such an infrastructure-led policy learning framework
Considerations about Continuous Experimentation for Resource-Constrained Platforms in Self-Driving Vehicles
Autonomous vehicles are slowly becoming reality thanks to the efforts of many
academic and industrial organizations. Due to the complexity of the software
powering these systems and the dynamicity of the development processes, an
architectural solution capable of supporting long-term evolution and
maintenance is required.
Continuous Experimentation (CE) is an already increasingly adopted practice
in software-intensive web-based software systems to steadily improve them over
time. CE allows organizations to steer the development efforts by basing
decisions on data collected about the system in its field of application.
Despite the advantages of Continuous Experimentation, this practice is only
rarely adopted in cyber-physical systems and in the automotive domain. Reasons
for this include the strict safety constraints and the computational
capabilities needed from the target systems.
In this work, a concept for using Continuous Experimentation for
resource-constrained platforms like a self-driving vehicle is outlined.Comment: Copyright 2017 Springer. Paper submitted and accepted at the 11th
European Conference on Software Architecture. 8 pages, 1 figure. Published in
Lecture Notes in Computer Science vol 10475 (Springer),
https://link.springer.com/chapter/10.1007/978-3-319-65831-5_
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