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

尽管自动驾驶的研究近年来取得了长足的进步，但完全自动驾驶的汽车仍然是一个遥远的目标，主要是因为缺乏健壮性。目前的自动驾驶汽车不能在新的道路上行驶，也不能在发生重大变化的道路(如地震后)上行驶，也不能在停车场、城市和隧道等有GPS或数据中断的道路上行驶。重要的是，人类擅长这一切：人类可以在没有详细地图或高精度GPS/IMU传感器的情况下开车，通常只需要少量稀少的信息来导航，而且他们的表现通常会随着时间的推移而变得更好。这项计划中的研究将以“智能”人类驾驶员为指导，开发出算法，能够以可测量的置信度实时感知和预测场景，特别是在场景离汽车更近的情况下。通过检测和克服近期(实时)和长期(学习)错误的能力，将实现新的稳健性特征。计划中的算法将以一种方式进行设计和验证，以实现目前自动驾驶所不具备的固有健壮性，并迅速被社区采用。该项目与NSF的智能物理系统(IPS)保持一致，因为算法将需要在知识丰富的环境中具有认知和反思能力。此外，该项目的成果将对机器人、机器学习和网络物理系统产生影响。在教育方面，将传播数据日志，以实现社区中的开放式学生项目，本科生和高中生将与研究团队合作，集成传感器，执行实验和数据收集，并将数据日志传播到社区。由机械和航空航天工程以及康奈尔大学计算机科学的研究人员领导，这项研究的目标是开发、集成和验证理论和算法，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，因为算法将需要在知识丰富的环境中具有认知和反思能力。该技术方法将为检测、场景估计、预测以及异常/错误检测和学习开发一个强大的感知管道；将算法集成到康奈尔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，以使社区能够更快地采用它们。待开发的关键组件级算法包括具有可量化性能的随时深度学习检测器；具有记忆属性的多假设推理；模拟人类对动态场景的心理模型的广义概率预测算法；以及结合在线学习的异常/错误检测。成果将包括开源算法和数据日志；出版物、会议、研讨会；课程和社区中开放式项目的数据日志；以及自动驾驶跨学科领域的本科生和高中教育和多样性项目。

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.