AD-VILS: Implementation and Reliability Validation of Vehicle-in-the-Loop Simulation Platform for Evaluating Autonomous Driving Systems

Vehicle-in-the-loop simulation (VILS) is a vehicle-testing technique that integrates high-fidelity simulation environments with real-world vehicles. Among existing simulation approaches for evaluating autonomous driving systems (ADS), VILS is particularly noteworthy because it faithfully reflects the dynamic characteristics of real-world vehicles and ensures repeatable and reproducible testing in diverse virtual scenarios. While researchers strive to implement a VILS platform that closely approximates real-world vehicle-testing environments, the performance of vehicles in VILS testing may differ from that observed in real-world testing, depending on the platform’s reliability. Therefore, methods must be established to validate the reliability of VILS platforms. Herein, we present the essential components of a VILS platform for evaluating ADS (AD-VILS) and propose a metho dology to validate the reliability of the implemented AD-VILS platform. This methodology includes scenario definition, techniques for VILS testing and real-world vehicle testing, and procedures for evaluating consistency and correlation based on statistical and mathematical comparisons between the datasets from virtual and real-world tests. Moreover, we empirically derive reliability evaluation criteria through iterative testing. This methodology aims to enhance the precision and reliability of ADS evaluations conducted on AD-VILS platforms.

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Agent Architecture for Adaptive Behaviors in Autonomous Driving

Evolution has endowed animals with outstanding adaptive behaviours which are grounded in the organization of their sensorimotor system. This paper uses inspiration from these principles of organization in the design of an artificial agent for autonomous driving. After distilling the relevant principles from biology, their functional role in the implementation of an artificial system are explained. The resulting Agent, developed in an EU H2020 Research and Innovation Action, is used to concretely demonstrate the emergence of adaptive behaviour with a significant level of autonomy. Guidelines to adapt the same principled organization of the sensorimotor system to other agents for driving are also obtained. The demonstration of the system abilities is given with example scenarios and open access simulation tools. Prospective developments concerning learning via mental imagery are finally discussed.

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In-Bore Dynamic Measurement and Mechanism Analysis of Multi-Physics Environment for Electromagnetic Railguns

Electromagnetic launch technology has important applications in many fields. However, the extremely harsh multi-physics environment during the launch is quite different from that of conventional guns. Little experimental research studied the dynamic distribution of the extreme impact environment and magnetic fields in the projectile. To this end, this paper designs a projectile-borne storage testing system for the dynamic measurement of harsh multi-physics environments. The detailed assessment of the measured dynamic multi-physics field shows that the velocity skin effect (VSE) is an important factor affecting the dynamic results. It causes a higher current density in the armature, and the magnetic induction and acceleration in the dynamic experiment are lower than those in the static-based experiment and simulation. Moreover, it causes the concentrated heat on the trailing edge of the armature, which lead to the melt-wave erosion, even affects the movement of integrated projectile during launch. Furthermore, the physical mechanism behind these phenomenon is revealed, and the causes of muzzle velocity error are analyzed. In conclusion, a feasible, dynamic measurement method for multi-physics coupled environments is presented, which can provide references for follow-up modeling and simulation researches and promote the development of railguns.

Published in the IEEE Magnetics Society Section within IEEE Access.

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Robots Under COVID-19 Pandemic: A Comprehensive Survey

As a result of the difficulties brought by COVID-19 and its associated lockdowns, many individuals and companies have turned to robots in order to overcome the challenges of the pandemic. Compared with traditional human labor, robotic and autonomous systems have advantages such as an intrinsic immunity to the virus and an inability for human-robot-human spread of any disease-causing pathogens, though there are still many technical hurdles for the robotics industry to overcome. This survey comprehensively reviews over 200 reports covering robotic systems which have emerged or have been repurposed during the past several months, to provide insights to both academia and industry. In each chapter, we cover both the advantages and the challenges for each robot, finding that robotics systems are overall apt solutions for dealing with many of the problems brought on by COVID-19, including: diagnosis, screening, disinfection, surgery, telehealth, care, logistics, manufacturing and broader interpersonal problems unique to the lockdowns of the pandemic. By discussing the potential new robot capabilities and fields they applied to, we expect the robotics industry to take a leap forward due to this unexpected pandemic.

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