Classical approaches and procedures for testing of automated vehicles of SAE levels 1 and 2 were based on defined scenarios with specific maneuvers, depending on the function under test. For automated driving systems (ADS) of SAE level 3+, the scenario space is infinite and calling for virtual testing and verification. However, even in simulation, the generation of safety-relevant scenarios for ADS is expensive and time-consuming. This leads to a demand for stochastic and realistic traffic simulation. Therefore, microscopic traffic flow simulation models (TFSM) are becoming a crucial part of scenario-based testing of ADS. In this paper, a co-simulation between the multi-body simulation software IPG CarMaker and the microscopic traffic flow simulation software (TFSS) PTV Vissim is used. Although the TFSS could provide realistic and stochastic behavior of the traffic participants, safety-critical scenarios (SCS) occur rarely. In order to avoid this, a novel Stress Testing Method (STM) is introduced. With this method, traffic participants are manipulated via external driver DLL interface from PTV Vissim in the vicinity of the vehicle under test in order to provoke defined critical maneuvers derived from statistical accident data on highways in Austria. These external driver models imitate human driving errors, resulting in an increase of safety-critical scenarios. As a result, the presented STM method contributes to an increase of safety-relevant scenarios for verification, testing and assessment of ADS.
The complexity of automated driving poses challenges for providing safety assurance. Focusing on the architecting of an Autonomous Driving Intelligence (ADI), i.e. the computational intelligence, sensors and communication needed for high levels of automated driving, we investigate so called safety supervisors that complement the nominal functionality. We present a problem formulation and a functional architecture of a fault-tolerant ADI that encompasses a nominal and a safety supervisor channel. We then discuss the sources of hazardous events, the division of responsibilities among the channel…
Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）；Hino Motors, Ltd.（日野汽车公司）
Lane change is a very demanding driving task and number of traffic accidents are induced by mistaken maneuvers. An automated lane change system has the potential to reduce driver workload and to improve driving safety. One challenge is how to improve driver acceptance on the automated system. From the viewpoint of human factors, an automated system with different styles would improve user acceptance as the drivers can adapt the style to different driving situations. This paper proposes a method to design different lane change styles in automated driving by analysis and modeling of truck driver…
While automated driving technology has achieved a tremendous progress, the scalable and rigorous testing and verification of safe automated and autonomous driving vehicles remain challenging. This paper proposes a learning-based falsification framework for testing the implementation of an automated or self-driving function in simulation. We assume that the function specification is associated with a violation metric on possible scenarios. Prior knowledge is incorporated to limit the scenario parameter variance and in a model-based falsifier to guide and improve the learning process. For an exe…
A new Taxonomy for Automated Driving: Structuring Applications based on their Operational Design Domain, Level of Automation and Automation Readiness
Technical University Munich（慕尼黑工业大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；TU Darmstadt（达姆施塔特工业大学）；Institute of Automotive Engineering（汽车工程研究所）
Jaguar Land Rover Limited（捷豹路虎有限公司）；University of Surrey（萨里大学）
Quantifying and encoding occupants' preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-complexity approach for lane-change initiation and planning to facilitate highly automated driving on freeways. Conditions under which human drivers find different manoeuvres desirable are learned from naturalistic driving data, eliminating the need for an engineered objective function and incorporation of expert knowledge in form of rules. Motion planning is formulated as a finite-horizon optimisation problem with saf…
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）
University of Alcalá（阿尔卡拉大学）；Joint Research Centre, European Commission（欧盟委员会联合研究中心）
Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow to evaluate safety with real behaviors for critical and edge cases, nor to evaluate the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predic…
Performance evaluation of a ROS2 based Automated Driving System
Fraunhofer Institute for Open Communication Systems (FOKUS)（弗劳恩霍夫开放通信系统研究所）；Daimler Center for Automotive Information Technology Innovations (DCAITI)（戴姆勒汽车信息技术创新中心）；University of Potsdam（波茨坦大学）
RWTH Aachen University（亚琛工业大学）；Institute for Automotive Engineering（汽车工程研究所）
Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. We focus on testing for verification and validation purposes at the interface between perception and planning, and structure our analysis along the three axes 1) test criteria and metrics, 2) test scenarios, and 3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. We find that the realiz…
With advances in sensing, computing, and communication technologies, Connected and Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new opportunities to improve the energy efficiency of individual vehicles. However, testing and verifying energy-efficient autonomous driving systems are difficult due to safety considerations and repeatability. In this paper, we present a co-simulation platform to develop and test novel vehicle eco-autonomous driving technologies named InfoRich, which incorporates the information from on-board sensors, V2X communications, and map datab…
Toward a Full-Stack Co-Simulation Platform for Testing of Automated Driving Systems
Institute of Automotive Engineering（汽车工程研究所）；Graz University of Technology（格拉茨理工大学）；College of Transportation Engineering（交通工程学院）；Tongji University（同济大学）
Impact of Level 2/3 Automated Driving Technology on Road Work Zone Safety
Faculty of Railway Transportation, Shanghai Institute of Technology（上海应用技术大学轨道交通学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
University of Michigan（密歇根大学）；University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）；State Farm Mutual Automobile Insurance Company（州立农场相互汽车保险公司）
In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers takeover timeliness and q…
Saarland University（萨尔大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
We present a novel hybrid learning method, HyLEAR, for solving the collision-free navigation problem for self-driving cars in POMDPs. HyLEAR leverages interposed learning to embed knowledge of a hybrid planner into a deep reinforcement learner to faster determine safe and comfortable driving policies. In particular, the hybrid planner combines pedestrian path prediction and risk-aware path planning with driving-behavior rule-based reasoning such that the driving policies also take into account, whenever possible, the ride comfort and a given set of driving-behavior rules. Our experimental perf…
Planning is an essential topic in the realm of automated driving. Besides planning algorithms that are widely covered in the literature, planning requires different software tools for its development, validation, and execution. This paper presents a survey of such tools including map representations, communication, traffic rules, open-source planning stacks and middleware, simulation, and visualization tools as well as benchmarks. We start by defining the planning task and different supporting tools. Next, we provide a comprehensive review of state-of-the-art developments and analysis of relat…
Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to humans, automated vehicles are supposed to perform anomaly detection. In this work, we propose the spatio-temporal graph auto-encoder for learning normal driving behaviours. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents. To perform anomaly detection, we first estimate a density function of the learned trajectory feature representation and then detect anomalies in low-density regions. Due to the lack of multi-agent trajectory datasets for anomaly detec…
FZI Research Center for Information Technology（FZI信息技术研究中心）；IPG Automotive GmbH（IPG汽车有限公司）
Verification and validation are major challenges for developing automated driving systems. A concept that gets more and more recognized for testing in automated driving is scenario-based testing. However, it introduces the problem of what scenarios are relevant for testing and which are not. This work aims to find relevant, interesting, or critical parameter sets within logical scenarios by utilizing Bayes optimization and Gaussian processes. The parameter optimization is done by comparing and evaluating six different metrics in two urban intersection scenarios. Finally, a list of ideas this w…
University of Waterloo（滑铁卢大学）；DENSO CORPORATION（电装公司）
Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system level and these efforts are missing the critical linking argument needed to integrate safety requirements at the system level with component performance requiremen…
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心（SnT））；University of Luxembourg（卢森堡大学）
Automated Driving Systems (ADS) have rapidly evolved in recent years and their architecture becomes sophisticated. Ensuring robustness, reliability and safety of performance is particularly important. The main challenge in building an ADS is the ability to meet certain stringent performance requirements in terms of both making safe operational decisions and finishing processing in real-time. Middlewares play a crucial role to handle these requirements in ADS. The way middlewares share data between the different system components has a direct impact on the overall performance, particularly the …
Robert Bosch GmbH（罗伯特·博世有限公司）；Intel Corporation（英特尔公司）；TWT Gmbh（TWT有限公司）
This paper represents the novel high precision localization approach for Automated Driving (AD) relative to 3D map. The AD maps are not necessarily flat. Hence, the problem of localization is solved here in 3D. The vehicle motion is modeled as piecewise planner but with vertical curvature which is approximated with clothoids. The localization problem is solved with Decentralized Kalman filter with feedback (DKFF) by fusing all available information. The odometry, visual odometry, GPS, the different sensor and mono camera inputs are fused together to obtain the precise localization relative to …
While the most visible part of the safety verification process of automated vehicles concerns the planning and control system, it is often overlooked that safety of the latter crucially depends on the fault-tolerance of the preceding environment perception. Modern perception systems feature complex and often machine-learning-based components with various failure modes that can jeopardize the overall safety. At the same time, a verification by for example redundant execution is not always feasible due to resource constraints. In this paper, we address the need for feasible and efficient percept…
Volkswagen AG（大众汽车公司）；Technische Universität Darmstadt（达姆施塔特工业大学）
General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement …
To maximize safety and driving comfort, autonomous driving systems can benefit from implementing foresighted action choices that take different potential scenario developments into account. While artificial scene prediction methods are making fast progress, an attentive human driver may still be able to identify relevant contextual features which are not adequately considered by the system or for which the human driver may have a lack of trust into the system's capabilities to treat them appropriately. We implement an approach that lets a human driver quickly and intuitively supplement scene p…
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Tübingen（蒂宾根大学）；Brandenburg University of Technology（勃兰登堡工业大学）；Ulm University（乌尔姆大学）；Esslingen University of Applied Sciences（埃斯林根应用科学大学）
Scenario-based approaches for the validation of highly automated driving functions are based on the search for safety-critical characteristics of driving scenarios using software-in-the-loop simulations. This search requires information about the shape and probability of scenarios in real-world traffic. The scope of this work is to develop a method that identifies redefined logical driving scenarios in field data, so that this information can be derived subsequently. More precisely, a suitable approach is developed, implemented and validated using a traffic scenario as an example. The presente…
Robert Bosch GmbH（罗伯特·博世有限公司）；Robert Bosch LLC（罗伯特·博世有限责任公司）
Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD)in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all available information and knowledge in the driving task. Knowledge graphs (KG) have recently gained significant attention from both industry and academia for applications that benefit by exploiting structured, dynamic, and relational data. The c…
NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving
ARC Training Centre for Automated Vehicles in Rural and Remote Regions (AVR3)（澳大利亚农村及偏远地区自动驾驶车辆ARC培训中心）；Université Gustave Eiffel（Gustave Eiffel大学）；Queensland University of Technology (QUT)（昆士兰科技大学）
Perception is a safety-critical function of autonomous vehicles and machine learning (ML) plays a key role in its implementation. This position paper identifies (1) perceptual uncertainty as a performance measure used to define safety requirements and (2) its influence factors when using supervised ML. This work is a first step towards a framework for measuring and controling the effects of these factors and supplying evidence to support claims about perceptual uncertainty.
This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and efficiency in autonomous freeway driving. We evaluate this hypothesis in a simulated scenario where an autonomous car must safely perform three lane changes in rapid succession. Approximate POMDP solutions are obtained through the partially observable Monte Carlo planning with observation widening (POMCPOW) algorithm. This approach outperforms over-confident and conservative MDP baselines and matches or outperforms QMDP…
From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Driving
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tianjin University（天津大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles. We use numerous dashcam crash videos uploaded on the internet to extract valuable collision data and recreate the crash scenarios in a simulator. We tackle the problem of extracting 3D vehicle trajectories from videos recorded by an unknown and uncalibrated monocular camera source using a modular approach. A working architecture and demonstration videos along with the open-source implementation are provided with the paper.
The University of Michigan（密歇根大学）；University of Pittsburgh（匹兹堡大学）；University of Michigan（密歇根大学）
It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests (TORs), and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset from…
Short-term future of automated driving can be imagined as a hybrid scenario in which both automated and human-driven vehicles co-exist in the same environment. In order to address the needs of such road configuration, many technology solutions such as vehicular communication and predictive control for automated vehicles have been introduced in the literature. Both aforementioned solutions rely on driving data of the human driver. In this work, we investigate the currently available driving datasets and introduce a real-world maneuver-based driving dataset that is collected during our urban dri…
Volkswagen AG（大众汽车公司）；Technische Universität Darmstadt（达姆施塔特工业大学）
Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a comp…
New Mobility Technologies Directorate（新移动技术局）；Connected Places Catapult（互联城市弹射中心）
The MUSICC project has created a proof-of-concept scenario database to be used as part of a type approval process for the verification of automated driving systems (ADS). This process must include a highly automated means of evaluating test results, as manual review at the scale required is impractical. This paper sets out a framework for assessing an ADS's behavioural safety in normal operation (i.e. performance of the dynamic driving task without component failures or malicious actions). Five top-level evaluation criteria for ADS performance are identified. Implementing these requires two ty…
