S&AS: INT: Inference, Reasoning, and Learning for Robust Autonomous Driving

虽然近年来自动驾驶的研究取得了很大进展，但完全自动驾驶汽车仍然是一个遥远的目标，主要是因为缺乏鲁棒性。目前的自动驾驶汽车不能在新的道路上行驶，也不能在已经发生重大变化的道路上行驶（比如地震后），也不能在GPS或数据中断的情况下行驶，比如在停车场、城市和隧道中。重要的是，人类擅长所有这些：人类可以在没有详细地图或高精度GPS/IMU传感器的情况下驾驶，并且通常只需要少量的稀疏信息来指导，并且随着时间的推移，他们的表现通常会随着学习而变得更好。计划中的研究将以“智能”人类驾驶员为指导，开发出能够以可测量的信心实时感知和预测场景的算法，特别是当场景离汽车更近时。新的鲁棒性特性将通过在短期（实时）和长期（学习）中检测和克服错误的能力来实现。计划中的算法将以一种固有的鲁棒性来设计和验证，这种鲁棒性目前在自动驾驶中是不具备的，并将被社区快速采用。该项目与NSF的智能物理系统（IPS）保持一致，因为算法需要在知识丰富的环境中具有认知和反射能力。此外，该项目的成果将影响机器人、机器学习和网络物理系统。在教育方面，数据日志将被传播，以实现社区开放式的学生项目，本科生和高中生将与研究团队合作，集成传感器，进行实验和数据收集，并向社区传播数据日志。由康奈尔大学机械和航空航天工程以及计算机科学的研究人员领导，这项研究的目标是开发、整合和验证理论和算法，以实现稳健和持久的自动驾驶。该项目与NSF的智能物理系统（IPS）保持一致，因为算法需要在知识丰富的环境中具有认知和反射能力。该技术方法将为检测、场景估计、预测、异常/错误检测和学习开发一个鲁棒的感知管道；将算法整合到康奈尔大学的自动驾驶汽车软件框架中，并在一系列实验场景中验证组件和系统，以使其更快地被社区采用。要开发的关键组件级算法包括具有可量化性能的任何时间深度学习检测器；具有记忆属性的多假设推理；广义概率预测算法模拟人类对动态场景的心理模型；异常/错误检测与在线学习相结合。成果将包括开源算法和数据日志；出版物、会议、讲习班；课程和整个社区开放式项目的数据日志；本科和高中学历以及自动驾驶跨学科领域的多元化项目。

While research in autonomous driving has made great strides in recent years, fully autonomous cars are still a distant goal, primarily because of a lack of robustness. Current autonomous cars cannot drive on new roads, or roads that have changed substantially (such as after an earthquake), or when there is a GPS or data outage such as in parking garages, urban cities and tunnels. Importantly, humans are good at all of this: Humans can drive without detailed maps or high precision GPS/IMU sensors, and typically require only a small amount of sparse information for guidance, and their performance typically gets better over time through learning. Using the "intelligent" human driver as a guide, the planned research will develop algorithms that can perceive and make predictions about a scene in real time with measurable confidence, particularly as the scene is closer to the car. New robustness characteristics will be achieved through the ability to detect and overcome mistakes, both in the near term (real time) and long term (learning). The planned algorithms will be designed and validated in a way to enable an inherent robustness not currently available in autonomous driving, and fast adoption by the community. This project is aligned with NSF's Intelligent Physical Systems (IPS) because the algorithms will require cognizant and reflective capabilities in a knowledge-rich environment. Additionally, outputs of this project will impact robotics, machine learning and cyber-physical systems. Educationally, data logs will be disseminated to enable open ended student projects in the community, and undergrad and high school students will collaborate with the research team to integrate sensors, perform experiments and data collection, and disseminate data logs to the community. Led by researchers in Mechanical and Aerospace Engineering, and Computer Science at Cornell University, the goal of this research is to develop, integrate and validate theory and algorithms to enable robust and persistent autonomous driving. This project is aligned with NSF's Intelligent Physical Systems (IPS) because the algorithms will require cognizant and reflective capabilities in a knowledge-rich environment. The technical approach will develop a robust perceptual pipeline for detection, scene estimation, prediction, and anomaly/mistake detection and learning; integrate the algorithms into Cornell's autonomous car software framework and validate the components and system in a series of experimental scenarios to enable their faster adoption by the community. Key component level algorithms to be developed include anytime deep learning detectors with quantifiable performance; multiple hypothesis reasoning with memory attributes; generalized probabilistic anticipation algorithms to mimic a human's mental model of a dynamic scene; and anomaly/mistake detection coupled with online learning. Outcomes will include open source algorithms and data logs; publications, conferences, workshops; data logs for open ended projects in courses and across the community; and undergrad and high school education and diversity programs in the interdisciplinary area of autonomous driving.