Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing
Università della Svizzera italiana (USI)（瑞士意大利语区大学）；Technical University of Munich（慕尼黑工业大学）；fortiss GmbH（fortiss有限公司）；Università degli Studi di Udine（乌迪内大学）
Matteo Biagiola, Andrea Stocco, Vincenzo Riccio, Paolo Tonella
Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simulators, either for in-house or outsourced testing, the generalizability of testing results to real autonomous vehicles is at stake. In this paper, we enhance simulation-based testing by introducing the notion of digital siblings, a multi-simulator approach that tests a given autonomous vehicle on multiple general-purpose simulators built with different technologies, that operate collectively as an ensemble in the testing process.
We exemplify our approach on a case study focused on testing the lane-keeping component of an autonomous vehicle. We use two open-source simulators as digital siblings, and we empirically compare such a multi-simulator approach against a digital twin of a physical scaled autonomous vehicle on a large set of test cases. Our approach requires generating and running test cases for each individual simulator, in the form of sequences of road points. Then, test cases are migrated between simulators, using feature maps to characterize the exercised driving conditions. Finally, the joint predicted failure probability is computed, and a failure is reported only in cases of agreement among the siblings.
Our empirical evaluation shows that the ensemble failure predictor by the digital siblings is superior to each individual simulator at predicting the failures of the digital twin. We discuss the findings of our case study and detail how our approach can help researchers interested in automated testing of autonomous driving software.
Vehicle Intelligence Pioneers Inc.（车智先锋公司）；University of Waterloo（滑铁卢大学）
Parallel driving is a novel framework to synthesize vehicle intelligence and transport automation. This article aims to define digital quadruplets in parallel driving. In the cyber-physical-social systems (CPSS), based on the ACP method, the names of the digital quadruplets are first given, which are descriptive, predictive, prescriptive and real vehicles. The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles. Then, the three virtual components of the digital quadruplets are introduced in detail and their applications are…
A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
Beijing Laboratory of Advanced Information Network（北京先进信息网络实验室）；Beijing Key Laboratory of Network System Architecture and Convergence（北京网络系统架构与收敛重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Information Systems Technology and Design Pillar（信息系统技术与设计学院）
How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence
Hubei University（湖北大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Beijing Huairou Academy of Parallel Sensing（北京怀柔平行传感研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Waytous Inc.（驭势科技（北京）有限公司）
Università della Svizzera italiana（瑞士意大利语大学）；University of Geneva（日内瓦大学）
Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field testing. Most testing techniques consider virtualized SDCs within a simulation environment, whereas less effort has been directed towards assessing whether such techniques transfer to and are effective with a physical real-world vehicle. In this paper, we shed light on the problem of generalizing testing results obtained in a driving simulator to a physical platform and provide a characterization and quantification of the sim2real gap affecting SDC testing. In our empirical study, we compare SDC testing whe…
ADDT -- A Digital Twin Framework for Proactive Safety Validation in Autonomous Driving Systems
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；University of California, Irvine（加州大学欧文分校）；Georgia Institute of Technology（佐治亚理工学院）；South China University of Technology（华南理工大学）
A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers
Department of Electrical Engineering, Eindhoven University of Technology（电子工程系，埃因霍温理工大学）；Siemens Digital Industries Software（西门子数字工业软件）；Department of Mechanical Engineering, Eindhoven University of Technology（机械工程系，埃因霍温理工大学）
Technical University of Delft（代尔夫特理工大学）；Siemens Digital Industries Software（西门子数字工业软件）
Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, unsafe, and time consuming. Algorithms are often developed first in simulation and then transferred to the real world, leading to a common sim2real challenge that performance decreases when the domain changes. In this paper, we propose a transfer learning process to minimize the gap by exploiting digital twin technology, relying on a systematic and simultaneous combination of virtual and real world data coming from ve…
Digital Twins in the Cloud: A Modular, Scalable and Interoperable Framework for Accelerating Verification and Validation of Autonomous Driving Solutions
Clemson University International Center for Automotive Research (CU-ICAR)（克莱姆森大学国际汽车研究中心（CU-ICAR））
CD-TWINSAFE: A ROS-enabled Digital Twin for Scene Understanding and Safety Emerging V2I Technology
C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems, Cairo, Egypt（认知驾驶研究与车辆系统实验室，埃及开罗）；Computer Science and Engineering Department - Faculty of Media Engineering and Technology（计算机科学与工程系-媒体工程与技术学院）；German University in Cairo, Egypt（埃及开罗德国大学）
EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application
School of Engineering and Design, Department of Mobility Systems Engineering, Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI)（工程与设计学院，移动系统工程系，汽车技术研究所及慕尼黑机器人与机器智能研究所（MIRMI））
DriveE2E: Closed-Loop Benchmark for End-to-End Autonomous Driving through Real-to-Simulation
The University of Hong Kong（香港大学）；AIR, Tsinghua University（清华研究院）；King’s College London（伦敦国王学院）；The Hong Kong Polytechnic University（香港理工大学）；Tuojing Intelligence（途镜智能）
Testing autonomous driving algorithms on real autonomous vehicles is extremely costly and many researchers and developers in the field cannot afford a real car and the corresponding sensors. Although several free and open-source autonomous driving stacks, such as Autoware and Apollo are available, choices of open-source simulators to use with them are limited. In this paper, we introduce the LGSVL Simulator which is a high fidelity simulator for autonomous driving. The simulator engine provides end-to-end, full-stack simulation which is ready to be hooked up to Autoware and Apollo. In addition…
Deep neural networks (DNNs) have achieved impressive performance on handling computer vision problems, however, it has been found that DNNs are vulnerable to adversarial examples. For such reason, adversarial perturbations have been recently studied in several respects. However, most previous works have focused on image classification tasks, and it has never been studied regarding adversarial perturbations on Image-to-image (Im2Im) translation tasks, showing great success in handling paired and/or unpaired mapping problems in the field of autonomous driving and robotics. This paper examines di…
Multisource human-in-the-loop digital twin testbed for connected and autonomous vehicles in mixed traffic flow
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动性学院）；Department of Civil and Environmental Engineering, University of Michigan（密歇根大学土木与环境工程系）
Cyber-Physical Systems (CPSs) combine software and physical components. These systems are widely applied in society within many domains, including the automotive, aerospace, railway, etc. Testing these systems is extremely challenging, therefore, it has attracted significant attention from the research community. A driving CPS testing technique in industry is simulation-based testing. However, this poses significant challenges. In this new-idea paper we present a novel approach to enhance the testing processes of CPSs. This novel approach is motivated with examples and open questions.
As a part of the digital transformation, we interact with more and more intelligent gadgets. Today, these gadgets are often mobile devices, but in the advent of smart cities, more and more infrastructure---such as traffic and buildings---in our surroundings becomes intelligent. The intelligence, however, does not emerge by itself. Instead, we need both design techniques to create intelligent systems, as well as approaches to validate their correct behavior. An example of intelligent systems that could benefit smart cities are self-driving vehicles. Self-driving vehicles are continuously becomi…
