[论文解读] Development Framework for Longitudinal Automated Driving Functions with Off-board Information Integration

Eric Armengaud, Sebastian Frager+5 more|arXiv (Cornell University)|Jun 18, 2019

Human-Automation Interaction and Safety参考文献 8被引用 5

一句话总结

本文提出了一种纵向自动驾驶功能的开发框架，通过数字可靠性身份（DDI）集成外部信息（如交通灯相位与配时、道路几何形状和限速）以确保系统行为的安全性、高效性和可组合性。该框架实现了从仿真到实时硬件在回路测试的无缝过渡，利用奥地利格拉茨市的真实数据，在城市环境中展示了更高的能效和驾驶员舒适度。

ABSTRACT

Increasingly sophisticated function development is taking place with the aim of developing efficient, safe and increasingly Automated Driving Functions. This development is possible with the use of diverse data from sources such as Navigation Systems, eHorizon, on-board sensor data, Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication. Increasing challenges arise with the dependency on large amounts of real-time data coming from off-board sources. At the core of addressing these challenges lies the concept of a Digital Dependability Identity (DDI) of a component or system. DDIs are modular, composable, and executable components in the field, facilitating: $\bullet$ efficient synthesis of component and system dependability information, $\bullet$ effective evaluation of information for safe and secure composition of highly distributed and autonomous Cyber Physical Systems. In AVL's Connected Powertrain (TM), Automated Driving Functions are tailored to Powertrain Control Strategies that predictively increase energy efficiency according to the powertrain type and its component efficiencies. Simultaneously, the burden on the driver is reduced by optimizing the vehicle velocity, whilst minimizing any journey time penalty.In this work, the development of dependable Automated Driving Functions is exemplified by the Traffic Light Assistant, an adaptive strategy that utilizes predictions of preceding traffic, upcoming road curvature, inclination, speed limits, and especially traffic light signal phase and timing information to increase the energy efficiency in an urban traffic environment. A key aspect of this development is the possibility for seamless and simultaneous development; from office simulation to human-in-the-loop and to real-time tests that include vehicle and powertrain hardware. Driver's acceptance and comfort is rated in an advanced diver simulator mounted on a hexapod, capable of emulating longitudinal and lateral acceleration of a real vehicle. Test results from real-time function validation on a Powertrain Testbed are shown, including real traffic light signal phasing information and traffic flow representation on Graz city roads.

研究动机与目标

• 解决将实时外部数据集成到自动驾驶功能中日益增长的复杂性问题。
• 确保自动驾驶中分布式信息物理系统的可靠性与安全组合。
• 实现从仿真、人机在回路到实时硬件环境的自动驾驶功能的无缝开发与验证。
• 通过利用预测的交通灯和道路状况数据，提升城市交通中的能效和驾驶员舒适度。
• 建立一种模块化、可组合且可执行的框架，用于基于数字可靠性身份（DDI）的系统级可靠性评估。

提出的方法

• 该框架采用数字可靠性身份（DDI）实时建模与验证组件和系统的可靠性。
• 集成包括eHorizon、V2I、V2V以及交通灯相位与配时（SPaT）信息在内的外部数据源。
• 通过预测道路曲率、坡度和限速，优化车辆速度与动力系统控制。
• 开发过程通过统一的流程从办公室仿真阶段推进到六自由度平台安装的驾驶模拟器上的人机在回路测试。
• 在动力系统测试台上进行实时验证，使用格拉茨市道路的实际交通灯相位与交通流数据。
• DDI支持在分布式组件间实现模块化、可组合且可执行的可靠性验证。

实验结果

研究问题

• RQ1 如何有效且可靠地将外部信息集成到纵向自动驾驶功能中？
• RQ2 何种架构框架能够实现从仿真到实时硬件测试的无缝过渡？
• RQ3 交通灯相位与配时数据的集成如何提升能效和驾驶员舒适度？
• RQ4 数字可靠性身份（DDI）在分布式自动驾驶系统中如何支持安全且可组合的系统构建？
• RQ5 预测性道路与交通数据对动力系统控制和车辆速度优化有何影响？

主要发现

• 该框架成功实现了基于格拉茨市道路实际交通灯信号相位数据的交通灯辅助功能的实时验证。
• 通过预测未来的交通灯相位并相应调整车辆速度，实现了能效的提升。
• 在基于六自由度平台的驾驶模拟器中进行的人机在回路仿真中，驾驶员舒适度和接受度获得积极评价。
• DDI的使用实现了在分布式组件间高效合成与评估可靠性信息。
• 系统在从仿真到硬件在回路测试的整个流程中表现出无缝集成，验证了该框架的可扩展性与可靠性。
• 该框架支持模块化、可组合且可执行的可靠性评估，对自动驾驶功能的安全部署至关重要。