9,390 research outputs found
From Software-Defined Vehicles to Self-Driving Vehicles: A Report on CPSS-Based Parallel Driving
On June 11th, 2017, the 28th IEEE Intelligent Vehicles Symposium (IV'2017) was held in Redondo Beach, California, USA. As one of the 8 workshops at IV'2017, the cyber-physical-social systems (CPSS)-based parallel driving (WS'08), organized by the State Key Laboratory for Management and Control of Complex Systems (SKL-MCCS), Institute of Automation, Chinese Academy of Sciences, China, Xi'an Jiaotong University, China, Tsinghua University, China, Indiana University-Purdue University Indianapolis, USA, and Cranfield University, U.K, has attracted both researchers and practitioners in intelligent vehicles. About 60-70 participants from various countries had extensive and deep discussions on definition, challenges and alternative solutions for CPSS-based parallel driving, and widely agreed that it is a novel paradigm of cloud-based automated driving technologies. Six speakers shared their ideas, studies, field applications, and vision for future along these emerging directions from software-defined vehicles to self-driving vehicles
Complete Agent-driven Model-based System Testing for Autonomous Systems
In this position paper, a novel approach to testing complex autonomous
transportation systems (ATS) in the automotive, avionic, and railway domains is
described. It is intended to mitigate some of the most critical problems
regarding verification and validation (V&V) effort for ATS. V&V is known to
become infeasible for complex ATS, when using conventional methods only. The
approach advocated here uses complete testing methods on the module level,
because these establish formal proofs for the logical correctness of the
software. Having established logical correctness, system-level tests are
performed in simulated cloud environments and on the target system. To give
evidence that 'sufficiently many' system tests have been performed with the
target system, a formally justified coverage criterion is introduced. To
optimise the execution of very large system test suites, we advocate an online
testing approach where multiple tests are executed in parallel, and test steps
are identified on-the-fly. The coordination and optimisation of these
executions is achieved by an agent-based approach. Each aspect of the testing
approach advocated here is shown to either be consistent with existing
standards for development and V&V of safety-critical transportation systems, or
it is justified why it should become acceptable in future revisions of the
applicable standards.Comment: In Proceedings FMAS 2021, arXiv:2110.1152
An Open Source and Open Hardware Deep Learning-Powered Visual Navigation Engine for Autonomous Nano-UAVs
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10 Watts of total power budget, have so far been considered incapable of running sophisticated visual-based autonomous navigation software without external aid from base-stations, ad-hoc local positioning infrastructure, and powerful external computation servers. In this work, we present what is, to the best of our knowledge, the first 27g nano-UAV system able to run aboard an end-to-end, closed-loop visual pipeline for autonomous navigation based on a state-of-the-art deep-learning algorithm, built upon the open-source CrazyFlie 2.0 nano-quadrotor. Our visual navigation engine is enabled by the combination of an ultra-low power computing device (the GAP8 system-on-chip) with a novel methodology for the deployment of deep convolutional neural networks (CNNs). We enable onboard real-time execution of a state-of-the-art deep CNN at up to 18Hz. Field experiments demonstrate that the system's high responsiveness prevents collisions with unexpected dynamic obstacles up to a flight speed of 1.5m/s. In addition, we also demonstrate the capability of our visual navigation engine of fully autonomous indoor navigation on a 113m previously unseen path. To share our key findings with the embedded and robotics communities and foster further developments in autonomous nano-UAVs, we publicly release all our code, datasets, and trained networks
FusionPlanner: A Multi-task Motion Planner for Mining Trucks using Multi-sensor Fusion Method
In recent years, significant achievements have been made in motion planning
for intelligent vehicles. However, as a typical unstructured environment,
open-pit mining attracts limited attention due to its complex operational
conditions and adverse environmental factors. A comprehensive paradigm for
unmanned transportation in open-pit mines is proposed in this research,
including a simulation platform, a testing benchmark, and a trustworthy and
robust motion planner. \textcolor{red}{Firstly, we propose a multi-task motion
planning algorithm, called FusionPlanner, for autonomous mining trucks by the
Multi-sensor fusion method to adapt both lateral and longitudinal control tasks
for unmanned transportation. Then, we develop a novel benchmark called
MiningNav, which offers three validation approaches to evaluate the
trustworthiness and robustness of well-trained algorithms in transportation
roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator
(PMS), a new high-fidelity simulator specifically designed for open-pit mining
scenarios. PMS enables the users to manage and control open-pit mine
transportation from both the single-truck control and multi-truck scheduling
perspectives.} \textcolor{red}{The performance of FusionPlanner is tested by
MiningNav in PMS, and the empirical results demonstrate a significant reduction
in the number of collisions and takeovers of our planner. We anticipate our
unmanned transportation paradigm will bring mining trucks one step closer to
trustworthiness and robustness in continuous round-the-clock unmanned
transportation.Comment: 2Pages, 10 figure
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
Thanks to the augmented convenience, safety advantages, and potential
commercial value, Intelligent vehicles (IVs) have attracted wide attention
throughout the world. Although a few autonomous driving unicorns assert that
IVs will be commercially deployable by 2025, their implementation is still
restricted to small-scale validation due to various issues, among which precise
computation of control commands or trajectories by planning methods remains a
prerequisite for IVs. This paper aims to review state-of-the-art planning
methods, including pipeline planning and end-to-end planning methods. In terms
of pipeline methods, a survey of selecting algorithms is provided along with a
discussion of the expansion and optimization mechanisms, whereas in end-to-end
methods, the training approaches and verification scenarios of driving tasks
are points of concern. Experimental platforms are reviewed to facilitate
readers in selecting suitable training and validation methods. Finally, the
current challenges and future directions are discussed. The side-by-side
comparison presented in this survey not only helps to gain insights into the
strengths and limitations of the reviewed methods but also assists with
system-level design choices.Comment: 20 pages, 14 figures and 5 table
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