MIT And Toyota Announce An Important Dataset For Improving Perception Research

麻省理工学院和丰田公司宣布了一个改善感知研究的重要数据集

The article “What Can Tesla

那篇文章《特斯拉能做什么》

TSLA And Waymo Learn From A Human’s Ability To Focus,” discusses some of the important differences between human perception systems and those employed by modern Autonomous Vehicles (AV). Recently, MIT and Toyota took a step towards human-like perception with the introduction of the DriveSeg research dataset.

特斯拉和Waymo从人类集中注意力的能力中学习，“探讨了人类感知系统与现代自动驾驶汽车(AV)所使用的感知系统之间的一些重要区别。 最近，麻省理工学院和丰田公司推出了DriveSeg研究数据集，向类人感知迈出了一步。

What is DriveSeg ?

什么是DriveSeg？

DriveSig has two parts. First, the data consists of 2 minutes and 47 seconds of high-resolution video captured during a daytime trip around the busy streets of Cambridge, Massachusetts. The video’s 5,000 frames are densely annotated manually with per-pixel human labels of 12 classes of road objects. Second, a set of videos captured from a range of scenes drawn from MIT Advanced Vehicle Technology (AVT) Consortium data coarsely annotated through a novel semiautomatic annotation approach developed by MIT.

DriveSig有两个部分。 首先，数据由一次白天，在马萨诸塞州剑桥繁忙街道周围旅行时，拍摄的2分47秒高分辨率视频组成。 视频的5000帧被密集地人工标注了12类道路物体的每像素人类标签。 第二，从一系列场景中捕获的一组视频，这些场景取自麻省理工学院先进车辆技术(AVT)联盟数据，通过麻省理工学院开发的一种新颖的半自动注释方法进行粗略注释。

Ok, why is this interesting or important ?

好吧，为什么这听上去很有趣或者很重要？

Humans use motion as a very strong part of object recognition. For example, it is not unusual to recognize a friend from afar simply by recognizing their gait. Thus, motion is an important part of human object recognition, but to date has not been a big part of Autonomous Vehicle(AV) object recognition. Rather, AV object recognition has traditionally focused on training AI engines on individual images. With this dataset, researchers from Massachusetts Institute for Technology (MIT) AgeLab at the MIT Center for Transportation & Logistics and the Toyota Collaborative Safety Research Center (CSRC) want to enable AI research focused on using movement.

人类使用动作作为物体识别的一个非常强的部分。 例如，仅仅通过识别一个远方的朋友的步态就能认出他们，这并不稀奇。 因此，运动是人类目标识别的一个重要部分，但到目前为止，运动还不是自动驾驶汽车（AV）目标识别的一个重要部分。 相反，AV对象识别传统上专注于在单个图像上训练AI引擎。 有了这个数据集，麻省理工学院运输与物流中心的AgeLab和丰田协同安全研究中心(CSRC)的研究人员想要让人工智能研究聚焦于使用移动。

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“In sharing this dataset, we hope to encourage researchers, the industry, and other innovators to develop new insight and direction into temporal AI modeling that enables the next generation of assisted driving and automotive safety technologies,” says Bryan Reimer, Research Scientist at the Agelab. “Our long-standing working relationship with Toyota CSRC has enabled our research efforts to impact future safety technologies.”

AGELAB的研究科学家Bryan Reimer表示: “通过共享这一数据集，我们希望鼓励研究人员，业界和其他创新者对当前人工智能建模提出新的见解和方向，从而实现下一代辅助驾驶和汽车安全技术。” 我们与丰田证监会长期的工作关系，使我们的研究努力能够冲击未来的安全技术。

MIT has a significant capability in artificial intelligence research with its Computer Science and Artificial Intelligence Laboratory (CSAIL) laboratory. This lab has been active in the area of robotics and AI with fundamental work announcements such as the recent announcement of “Giving soft robots feeling.” In contrast, the Age Lab at MIT’s Center for Transportation emphasizes a multidisciplinary approach which they believe will be necessary to crack the reality of Autonomous Vehicles.

麻省理工学院的计算机科学和人工智能实验室(CSAIL)在人工智能研究方面，具有重要的能力。 该实验室在机器人技术和人工智能领域一直很活跃，并宣布了一些基本工作，比如最近宣布的“给软机器人感觉”。相比之下，麻省理工学院交通运输中心的年龄实验室强调一种多学科的方法，他们认为这是破解自动驾驶汽车现实所必需的。

“Engineers are investing significant creative and financial resources in developing higher levels of automation to support drivers to be ideally both increasingly comfortable and safe. However, true success requires a human-centered engineering perspective. It’s more important than ever to leverage the expertise of other disciplines that amplify each other’s capabilities. For instance, computer scientists have the ability to mine and model large scale complex systems, while human factors engineers and psychologists are trained in developing an understanding of human behavior to ensure systems are optimally used in a way that enhances accessible and safe mobility,” said Bryan Reimer.

“工程师们正在投入大量的创造性和财政资源来开发更高水平的自动化，以支持驾驶员在理想的情况下既越来越舒适又越来越安全。 然而，真正的成功需要以人为中心的工程视角。 利用其他学科的专业知识来增强彼此的能力比以往任何时候都重要。 例如，计算机科学家有能力挖掘和建模大规模复杂系统，而人为因素工程师和心理学家则接受培训，了解人类行为，以确保系统以提高可访问性和安全性的方式得到最佳使用。”

To enable this effort, data were drawn from the Massachusetts Institute of Technology’s Advanced Vehicle Technology (AVT) Consortium,an academic-industry partnership drawing together stakeholders across the automotive ecosystem to collaboratively invest in the development of real-world data to better understanding how drivers adapt to, use (or do not use), and behave with production level vehicle technologies including advanced driver assistance systems and automated driving. By encompassing data from a range of real-world driving scenarios the DriveSeg dataset aims to provide variations in situations that facilitate deeper exploration of the value of motion in AV object recognition.

为了实现这一目标，该数据来自麻省理工学院的先进车辆技术(AVT)联盟，该联盟是一个校企合作伙伴关系，将汽车生态系统的利益相关者聚集在一起，共同投资于开发真实世界数据，以更好地了解驾驶员如何适应，使用（或不使用）生产级车辆技术，包括先进驾驶员辅助系统和自动驾驶。 DriveSeg数据集通过包含来自一系列真实世界驾驶场景的数据，旨在提供各种情况下的变化，以便于更深入地探索AV对象识别中运动的价值。

Overall, humans use concepts such as focus, abstraction, differential perception, and sensitivity to relative acceleration as key mechanisms to drive perception. Today’s AI engines have tended to rely on brute-force methods with limited success. This MIT/Toyota CSRC combination should allow the use of an important additional learning dimension (movement) to advance the state-of-art forward.

总体而言，人类使用诸如焦点，抽象，差异感知，以及对相对加速度的敏感度等概念作为驱动感知的关键机制。 今天的AI引擎已经倾向于依靠蛮力的方法，并且成果可见一斑。 麻省理工学院/丰田证监会的组合应该允许使用一个重要的额外学习维度（运动）来推进技术的发展。

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