There is an urgent societal need to assess whether autonomous vehicles (AVs) are safe enough. From published quantitative safety and reliability assessments of AVs, we know that, given the goal of predicting very low rates of accidents, road testing alone requires infeasible numbers of miles to be driven. However, previous analyses do not consider any knowledge prior to road testing - knowledge which could bring substantial advantages if the AV design allows strong expectations of safety before road testing. We present the advantages of a new variant of Conservative Bayesian Inference (CBI), which uses prior knowledge while avoiding optimistic biases. We then study the trend of disengagements (take-overs by human drivers) by applying Software Reliability Growth Models (SRGMs) to data from Waymo's public road testing over 51 months, in view of the practice of software updates during this testing. Our approach is to not trust any specific SRGM, but to assess forecast accuracy and then improve forecasts. We show that, coupled with accuracy assessment and recalibration techniques, SRGMs could be a valuable test planning aid.
Heriot-Watt University（赫瑞瓦特大学）；City, University of London（伦敦大学城市学院）
Context: Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem. Diverse evidence needs to be combined in a rigorous way: in particular, results of operational testing with other evidence from design and verification. Growing use of machine learning in SCSs, by precluding most established methods for gaining assurance, makes operational testing even more important for supporting safety and reliability claims. Objective: We use Autonomous Vehicles (AVs) as a current example to revisit the problem of demonstrating high reliability. AVs are making thei…
University of South Australia（南澳大利亚大学）；University of Tasmania（塔斯马尼亚大学）；University of Technology Sydney（悉尼科技大学）
Autonomous driving has been at the forefront of public interest, and a pivotal debate to widespread concerns is safety in the transportation system. Deep reinforcement learning (DRL) has been applied to autonomous driving to provide solutions for obstacle avoidance. However, in a road traffic junction scenario, the vehicle typically receives partial observations from the transportation environment, while DRL needs to rely on long-term rewards to train a reliable model by maximising the cumulative rewards, which may take the risk when exploring new actions and returning either a positive reward…
Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review
Qatar University（卡塔尔大学）；The University of British Columbia（不列颠哥伦比亚大学）；Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学）；KINDI Center for Computing Research（金迪计算研究中心）；Qatar Mobility Innovations Center (QMIC)（卡塔尔移动创新中心）；Manchester Metropolitan University（曼彻斯特城市大学）
Intelligent Embedded Systems Lab, University of Kassel（卡塞尔大学智能嵌入式系统实验室）；FORWISS, University of Passau（帕绍大学FORWISS）；KoopAutoV, University of Applied Sciences Aschaffenburg（阿沙芬堡应用科学大学KoopAutoV）
Highly automated driving requires precise models of traffic participants. Many state of the art models are currently based on machine learning techniques. Among others, the required amount of labeled data is one major challenge. An autonomous learning process addressing this problem is proposed. The initial models are iteratively refined in three steps: (1) detection and context identification, (2) novelty detection and active learning and (3) online model adaption.
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）
V-SenseDrive: A Privacy-Preserving Road Video and In-Vehicle Sensor Fusion Framework for Road Safety & Driver Behaviour Modelling
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST)（电气工程与计算机科学学院（SEECS），国立科学与技术大学（NUST））
Chalmers University of Technology（查尔姆斯理工大学）；University of Gothenburg（哥德堡大学）；RISE Research Institutes of Sweden（瑞典国家研究院）
The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally automated (e.g., on highways) and automated vehicles do not exist yet and leave researchers and practitioners with different challenges. For instance, current test procedures do not suffice for fully automated vehicles, which are supposed to be completely in charge for the driving task and have no driver as a back up. This paper presents current challenges of …
This article describes an approach to detect the wearing location of smart devices worn by pedestrians and cyclists. The detection, which is based solely on the sensors of the smart devices, is important context-information which can be used to parametrize subsequent algorithms, e.g. for dead reckoning or intention detection to improve the safety of vulnerable road users. The wearing location recognition can in terms of Organic Computing (OC) be seen as a step towards self-awareness and self-adaptation. For the wearing location detection a two-stage process is presented. It is subdivided into …
Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）；Sun Yat-sen University（中山大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction
University College London (UCL)（伦敦大学学院）；Peking University (PKU)（北京大学）；Massachusetts Institute of Technology (MIT)（麻省理工学院）；University of Florida（佛罗里达大学）
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Zhongguancun Laboratory（中关村实验室）；Beihang University（北京航空航天大学）；Institute of Data Space, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；A*STAR（新加坡科技研究局）
This paper presents the design of a supervisory algorithm that monitors safety at road intersections and overrides drivers with a safe input when necessary. The design of the supervisor consists of two parts: safety verification and control design. Safety verification is the problem to determine if vehicles will be able to cross the intersection without colliding with current drivers' inputs. We translate this safety verification problem into a jobshop scheduling problem, which minimizes the maximum lateness and evaluates if the optimal cost is zero. The zero optimal cost corresponds to the ca…
Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya（互联网跨学科研究所（IN3），加泰罗尼亚开放大学）
Increased interaction between and among pedestrians and vehicles in the crowded urban environments of today gives rise to a negative side-effect: a growth in traffic accidents, with pedestrians being the most vulnerable elements. Recent work has shown that Convolutional Neural Networks are able to accurately predict accident rates exploiting Street View imagery along urban roads. The promising results point to the plausibility of aided design of safe urban landscapes, for both pedestrians and vehicles. In this paper, by considering historical accident data and Street View images, we detail how…
The safety of single-vehicle autonomous driving technology is limited due to the limits of perception capability of on-board sensors. In contrast, vehicle-road collaboration technology can overcome those limits and improve the traffic safety and efficiency, by expanding the sensing range, improving the perception accuracy, and reducing the response time. However, such a technology is still under development; it requires rigorous testing and verification methods to ensure the reliability and trustworthiness of the technology. In this thesis, we focus on three major tasks: (1) analyze the functi…
Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can ensure safety without comprising the original purposes, by learning only high-level decisions from expert data and a low-level decentralized controller guided by the jointly learned decentralized barrier certificates. Empirical results show that our learned road user simulation models can achieve a significant improvement in safety comparing to state-of-the-art imitation learning and pure control-based methods, while b…
