Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art
German Aerospace Center (DLR) e.V.（德国航空航天中心）；Robert Bosch GmbH（罗伯特·博世有限公司）；FZI Research Center for Information Technology（FZI信息技术研究中心）；AVL Deutschland GmbH（德国AVL有限公司）
Lukas Westhofen, Christian Neurohr, Tjark Koopmann, Martin Butz, Barbara Schütt, Fabian Utesch, Birte Kramer, Christian Gutenkunst, Eckard Böde
发表信息：
Archives of Computational Methods in Engineering (2022) 1886-1784
The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application's requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle's safety performance are a key requirement for demonstrating its trustworthiness.
Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Renault SAS（雷诺股份有限公司）
In this paper, a multi-modal 360$^{\circ}$ framework for 3D object detection and tracking for autonomous vehicles is presented. The process is divided into four main stages. First, images are fed into a CNN network to obtain instance segmentation of the surrounding road participants. Second, LiDAR-to-image association is performed for the estimated mask proposals. Then, the isolated points of every object are processed by a PointNet ensemble to compute their corresponding 3D bounding boxes and poses. Lastly, a tracking stage based on Unscented Kalman Filter is used to track the agents along ti…
Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model
Graz University of Technology（格拉茨工业大学）；Institute of Automotive Engineering（汽车工程研究所）；College of Transportation Engineering（交通运输工程学院）；Tongji University（同济大学）
Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety
Technical University of Munich（慕尼黑工业大学）；TUM School of Engineering and Design（慕尼黑工业大学工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Institute of Automotive Technology（汽车技术研究所）；Tier IV（Tier IV公司）
Disengagement Analysis and Field Tests of a Prototypical Open-Source Level 4 Autonomous Driving System
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich（自动驾驶车辆系统教授职位，慕尼黑技术大学工程与设计学院，技术大学慕尼黑）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））
SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling
School of Traffic and Transportation, Harbin Institute of Technology（哈尔滨工业大学交通学院）；Chongqing Research Institute of HIT（哈尔滨工业大学重庆研究所）；Chongqing Changan Automobile Company Ltd（重庆长安汽车有限公司）
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；NCSR Demokritos（希腊国家科学研究中心德谟克利特）
Traversability illustrates the difficulty of driving through a specific region and encompasses the suitability of the terrain for traverse based on its physical properties, such as slope and roughness, surface condition, etc. In this survey we highlight the merits and limitations of all the major steps in the evolution of traversability estimation techniques, covering both non-trainable and machine-learning methods, leading up to the recent proliferation of deep learning literature. We discuss how the nascence of Deep Learning has created an opportunity for radical improvement in traversabilit…
An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions
Stuttgart Media University（斯图加特媒体大学）；Fraunhofer-Gesellschaft e.V.（弗劳恩霍夫协会）；Fraunhofer EMI（弗劳恩霍夫应用信息技术研究所）；Fraunhofer IVI（弗劳恩霍夫交通与基础设施系统研究所）；Fraunhofer IOSB（弗劳恩霍夫光学、微系统和图像处理研究所）；GOTECH GmbH（高泰克有限公司）；Technische Universität München（慕尼黑工业大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；understandAI GmbH（安代人工智能有限公司）；Porsche Engineering Group GmbH（保时捷工程集团有限公司）
Autonomous vehicles should be able to generate accurate probabilistic predictions for uncertain behavior of other road users. Moreover, reactive predictions are necessary in highly interactive driving scenarios to answer "what if I take this action in the future" for autonomous vehicles. There is no existing unified framework to homogenize the problem formulation, representation simplification, and evaluation metric for various prediction methods, such as probabilistic graphical models (PGM), neural networks (NN) and inverse reinforcement learning (IRL). In this paper, we formulate a probabili…
Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）；AUDI AG（奥迪股份公司）
Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles. We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-mod…
For a successful market launch of automated vehicles (AVs), proof of their safety is essential. Due to the open parameter space, an infinite number of traffic situations can occur, which makes the proof of safety an unsolved problem. With the so-called scenario-based approach, all relevant test scenarios must be identified. This paper introduces an approach that finds particularly challenging scenarios from real driving data (\RDDwo) and assesses their difficulty using a novel metric. Starting from the highD data, scenarios are extracted using a hierarchical clustering approach and then assign…
DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks
University of Science and Technology of China（中国科学技术大学）；Georgia Institute of Technology（佐治亚理工学院）；Xiaomi EV（小米电动车）；Fudan University（复旦大学）；Xidian University（西安电子科技大学）；South China University of Technology（华南理工大学）；The University of Hong Kong（香港大学）
IAV GmbH（IAV有限公司）；MHP Management- und IT-Beratung GmbH（MHP管理与IT咨询有限公司）；Technische Universität Berlin（柏林工业大学）
To validate the safety of automated vehicles (AV), scenario-based testing aims to systematically describe driving scenarios an AV might encounter. In this process, continuous inputs such as velocities result in an infinite number of possible variations of a scenario. Thus, metamodels are used to perform analyses or to select specific variations for examination. However, despite the safety criticality of AV testing, metamodels are usually seen as a part of an overall approach, and their predictions are not questioned. This paper analyzes the predictive performance of Gaussian processes (GP), de…
