Waymo's mission to reduce traffic injuries and fatalities and improve mobility for all has led us to expand deployment of automated vehicles on public roads without a human driver behind the wheel. As part of this process, Waymo is committed to providing the public with informative and relevant data regarding the demonstrated safety of Waymo's automated driving system, which we call the Waymo Driver. The data presented in this paper represents more than 6.1 million miles of automated driving in the Phoenix, Arizona metropolitan area, including operations with a trained operator behind the steering wheel from calendar year 2019 and 65,000 miles of driverless operation without a human behind the steering wheel from 2019 and the first nine months of 2020. The paper includes every collision and minor contact experienced during these operations as well as every predicted contact identified using Waymo's counterfactual, what if, simulation of events had the vehicle's trained operator not disengaged automated driving. There were 47 contact events that occurred over this time period, consisting of 18 actual and 29 simulated contact events, none of which would be expected to result in severe or life threatening injuries. This paper presents the collision typology and severity for each actual and simulated event, along with diagrams depicting each of the most significant events. Nearly all the events involved one or more road rule violations or other errors by a human driver or road user, including all eight of the most severe events, which we define as involving actual or expected airbag deployment in any involved vehicle. When compared to national collision statistics, the Waymo Driver completely avoided certain collision modes that human driven vehicles are frequently involved in, including road departure and collisions with fixed objects.
This report presents Waymo's proposal for a systematic fatigue risk management framework that addresses prevention, monitoring, and mitigation of fatigue-induced risks during on-road testing of ADS technology. The proposed framework remains flexible to incorporate continuous improvements, and was informed by state of the art practices, research, learnings, and experience (both internal and external to Waymo). Fatigue is a recognized contributory factor in a substantial fraction of on-road crashes involving human drivers, and mitigation of fatigue-induced risks is still an open concern research…
This paper describes Waymo's Collision Avoidance Testing (CAT) methodology: a scenario-based testing method that evaluates the safety of the Waymo Driver Automated Driving Systems' (ADS) intended functionality in conflict situations initiated by other road users that require urgent evasive maneuvers. Because SAE Level 4 ADS are responsible for the dynamic driving task (DDT), when engaged, without immediate human intervention, evaluating a Level 4 ADS using scenario-based testing is difficult due to the potentially infinite number of operational scenarios in which hazardous situations may unfol…
Understanding human driving behavior is crucial to develop autonomous vehicles' algorithms. However, most low level automation, such as the one in advanced driving assistance systems (ADAS), is based on objective safety measures, which are not always aligned with what the drivers perceive as safe and their correspondent driving behavior. Finding the bridge between the subjective perception and objective safety measures has been analyzed in this paper focusing specifically on lane-change scenarios. Results showed statistically significant differences between what is perceived as safe by drivers…
Waymo's safety methodologies, which draw on well established engineering processes and address new safety challenges specific to Automated Vehicle technology, provide a firm foundation for safe deployment of Waymo's Level 4 ADS, which Waymo also refers to as the Waymo Driver. Waymo's determination of its readiness to deploy its AVs safely in different settings rests on that firm foundation and on a thorough analysis of risks specific to a particular Operational Design Domain. Waymo's process for making these readiness determinations entails an ordered examination of the relevant outputs from a…
FZI Research Center for Information Technology（FZI信息技术研究中心）
Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants. However, human road users usually do not adhere to these rules strictly, resulting in varying degrees of rule conformity. Such deviations from given rules are key components of today's road traffic. In autonomous driving, robotic agents can disturb traffic flow, when rule deviations are not taken into account. In this paper, we present an approach to derive the distribution of degrees of rule conformity from human driving data. We demonstrate our method with the Waymo Open Motion data…
Virtual Roads, Smarter Safety: A Digital Twin Framework for Mixed Autonomous Traffic Safety Analysis
Zachry Department of Civil and Environmental Engineering, Texas A&M University（土木与环境工程系，德克萨斯A&M大学）；Department of Landscape Architecture and Urban Planning（景观建筑与城市规划系）
As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situations such as merges, unprotected turns, etc., where predicting individual object motion is not sufficient. Joint predictions of multiple objects are required for effective route planning. There has been a critical need for high-quality motion data that is rich in both interactions and annotation to develop motion planning models. In this work, we introduce the most diverse interactive motion dataset to our knowledge, a…
FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of …
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）
Various automobile and mobility companies, for instance Ford, Uber and Waymo, are currently testing their pre-produced autonomous vehicle (AV) fleets on the public roads. However, due to rareness of the safety-critical cases and, effectively, unlimited number of possible traffic scenarios, these on-road testing efforts have been acknowledged as tedious, costly, and risky. In this study, we propose Accelerated De- ployment framework to safely and efficiently estimate the AVs performance on public streets. We showed that by appropriately addressing the gradual accuracy improvement and adaptively…
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…
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 …
Despite the successful demonstration of autonomous vehicles (AVs), such as self-driving cars, ensuring AV safety remains a challenging task. Although some actors influence an AV's driving decisions more than others, current approaches pay equal attention to each actor on the road. An actor's influence on the AV's decision can be characterized in terms of its ability to decrease the number of safe navigational choices for the AV. In this work, we propose a safety threat indicator (STI) using counterfactual reasoning to estimate the importance of each actor on the road with respect to its influe…
Smart Roads: Roadside Perception, Vehicle-Road Cooperation and Business Model
Xidian University（西安电子科技大学）；Guangzhou Institute of Technology, Xidian University（西安电子科技大学广州研究院）；Eindhoven University of Technology（埃因霍温理工大学）；Nanyang Technological University（南洋理工大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（西穆拉大都会数字工程中心）
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and G…
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines.
The University of Sydney（悉尼大学）；Cohda Wireless（科达无线）
Cooperative perception, or collective perception (CP) is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). We present three different experiments to demonstrate the use of CP service within intelligent infrastructu…
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）
