感知融合 awesome list
感知融合 awesome list
雷达聚类
雷达处理杂波滤除 CFAR (Constant False Alarm Rate):Lee, Jae-Eun, et al. "Harmonic clutter recognition and suppression for automotive radar sensors." International Journal of Distributed Sensor Networks 13.9 (2017):1550147717729793.
德州仪器详尽雷达资料: Tracking radar targets with multiple reflection points (Texas Instruments)
雷达鬼影检测: Sole, Amir, et al. "Solid or not solid: Vision for radar target validation." IEEE Intelligent Vehicles Symposium, 2004. IEEE, 2004.
目标匹配
目标级融合详细步骤 Chavez-Garcia, Ricardo Omar, and Olivier Aycard. "Multiple sensor fusion and classification for moving object detection and tracking." IEEE Transactions on Intelligent Transportation Systems 17.2 (2015): 525-534.
摄像头雷达融合: Darms, Michael S., Paul E. Rybski, Christopher Baker, and Chris Urmson. "Obstacle detection and tracking for the urban challenge." IEEE Transactions on intelligent transportation systems 10, no. 3 (2009): 475-485.
激光雷达摄像头融合: Ziguo Zhong, Stanley Liu, Manu Mathew, and Aish Dubey.“Camera Radar Fusion for Increased Reliability in
ADAS Applications”. Electronic Imaging 2018.17 (2018):
自动泊车
同济车位识别详细步骤和数据集 Li, Linshen, et al. "Vision-based parking-slot detection: A benchmark and a learning-based approach." 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017.
同济深度学习角点检测算法:Zhang, Lin, et al. "Vision-based parking-slot detection: A DCNN-based approach and a large-scale benchmark dataset." IEEE Transactions on Image Processing 27.11 (2018): 5350-5364.
分割 Wu, Yan, et al. "VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View." 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018.
vslam: Huang, Yewei, et al. "Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking." 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018
超声波信号处理:Shao, Yunfeng, Pengzhen Chen, and Tongtong Cao. "A Grid Projection Method Based on Ultrasonic Sensor for Parking Space Detection." IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018.
融合:Suhr, Jae Kyu, and Ho Gi Jung. "Sensor fusion-based vacant parking slot detection and tracking." IEEE Transactions on Intelligent Transportation Systems 15.1 (2013): 21-36.
车道线
Bosch车道线识别: Klotz, Albrecht, Jan Sparbert, and Dieter Hoetzer. "Lane data fusion for driver assistance systems." Proc. 7th International Conference on Information Fusion, Stockholm, Sweden. 2004.
车辆与车道线关系: Nguyen, VanQuang, et al. "A study on real-time detection method of lane and vehicle for lane change assistant system using vision system on highway." Engineering science and technology, an international journal 21.5 (2018): 822-833.
深度学习方法lanenet: Neven, Davy, et al. "Towards end-to-end lane detection: an instance segmentation approach." 2018 IEEE intelligent vehicles symposium (IV). IEEE, 2018.
车道定位 Real-Time Global Localization of Robotic Cars in Lane Level via Lane Marking Detection and Shape Registration Dixiao Cui, Jianru 4 Xue, Member, IEEE, and Nanning Zheng, Fellow, IEEE
态势预估
cut-in预测,通过特征工程与机器学习预测切入目标:Heinemann, Tonja. Predicting cut-ins in traffic using a neural network. MS thesis. 2017.
Zhu, Ying, et al. "Reliable detection of overtaking vehicles using robust information fusion." IEEE Transactions on Intelligent Transportation Systems 7.4 (2006): 401-414.
激光雷达
ego-motion估计自车位置: Hoang, Berntsson. "Localisation using LiDAR and Camera." MS thesis. 2017.
车辆检测pipeline:Du, Xinxin, et al. "A general pipeline for 3d detection of vehicles." 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018.
博士论文:Zhongzhen Luo, LiDAR Based Perception System: Pioneer Technology for Safety Driving
相机融合:Car Detection for Autonomous Vehicle: LIDAR and Vision Fusion Approach Through Deep Learning Framework, Xinxin Du, Marcelo H. Ang Jr. and Daniela Rus
标定: Improvements to Target-Based 3D LiDAR to Camera Calibration, Jiunn-Kai Huang and Jessy W. Grizzle
机器视觉
SOC系统设计: Zhou, Yuteng. "Computer Vision System-On-Chip Designs for Intelligent Vehicles." (2018).
mobiley测距单目测速测距:Stein, Gideon P., Ofer Mano, and Amnon Shashua. "Vision-based ACC with a single camera: bounds on range and range rate accuracy." IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No. 03TH8683). IEEE, 2003.
mobileye行人检测:Pedestrian Detection for Driving Assistance Systems: Single-frame Classification
and System Level Performance
SLAM
- GraphSLAM: Sebastian Thrun, Michael Montemerlo. "The GraphSLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures"
深度学习
resnet: He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
yolo目标检测:Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
feature可视化:Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding convolutional networks." European conference on computer vision. Springer, Cham, 2014.
速度效果trade-off:Huang, Jonathan, et al. "Speed/accuracy trade-offs for modern convolutional object detectors." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
场景识别
场景综述:Xue, Jian-Ru, Jian-Wu Fang, and Pu Zhang. "A survey of scene understanding by event reasoning in autonomous driving." International Journal of Automation and Computing 15.3 (2018): 249-266.
动作识别: Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos. In: Proceedings of Advances in Neural Information Processing Systems. Red Hook, NY: Curran Associates, Inc., 2014. 568-576
fcn街景分割:Zhang Y, Qiu Z, Yao T, et al. Fully convolutional adaptation networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 6810-6818.
系统设计
ADAS handbook: Hermann Winner, Stephan Hakuli, Felix Lotz, Christina Singer.
“Handbook of Driver Assistance Systems" (2016)系统从需求功能到详细设计Automotive Systems Engineering, Markus Maurer, Hermann Winner
感知融合 awesome list的更多相关文章
- SystemML大规模机器学习,优化算子融合方案的研究
SystemML大规模机器学习,优化算子融合方案的研究 摘要 许多大规模机器学习(ML)系统允许通过线性代数程序指定定制的ML算法,然后自动生成有效的执行计划.在这种情况下,优化的机会融合基本算子的熔 ...
- 百度Apollo无人驾驶入门课程下载
本文提供 百度Apollo官网的无人驾驶入门课程下载,主要为视频文件. 视频数量:101个:文件格式:MP4:视频总时长:2小时40分钟:文件总大小:约1.13GB: 马上下载 关注公众号罗孚传说(R ...
- Mobileye 自动驾驶策略(一)
Mobileye 自动驾驶策略(一) 详解 Mobileye 自动驾驶解决方案 Mobileye的自动驾驶解决方案.总得来说,分为四种: Visual perception and sensor fu ...
- 13万字详细分析JDK中Stream的实现原理
前提 Stream是JDK1.8中首次引入的,距今已经过去了接近8年时间(JDK1.8正式版是2013年底发布的).Stream的引入一方面极大地简化了某些开发场景,另一方面也可能降低了编码的可读性( ...
- SLAM+语音机器人DIY系列:(三)感知与大脑——2.带自校准九轴数据融合IMU惯性传感器
摘要 在我的想象中机器人首先应该能自由的走来走去,然后应该能流利的与主人对话.朝着这个理想,我准备设计一个能自由行走,并且可以与人语音对话的机器人.实现的关键是让机器人能通过传感器感知周围环境,并通过 ...
- 【Apollo自动驾驶源码解读】车道线的感知和高精地图融合
模式选择 在modules/map/relative_map/conf/relative_map_config.pb.txt文件中对模式进行修改: lane_source: OFFLINE_GENER ...
- 什么是业务运维,企业如何实现互联网+业务与IT的融合
业务运维并不是一个新概念,针对传统信息架构提出的业务服务管理就是把以业务为核心的IT系统与IT基础设施性能进行整合运维的解决方案.然而随着互联网+转型的不断推进,基础设施的智能化和广泛云化成为IT发展 ...
- CTO对话:云端融合下的移动技术创新
云端融合真的来了?快听CTO们怎么讲云端融合下,技术创新怎么破? 快听CTO箴言 云喊了很多年,对于很多普通的技术人,心中有很多疑问:云端融合到底意味着什么,对公司的技术体系有什么影响,未来又会走向 ...
- FNN模糊神经网络——信息系统客户服务感知评价
案例描述 信息系统是否真正减轻业务人员的日常工作量提高工作效率?如何从提供“被动”服务转变为根据客户感知提供“主动”服务,真正实现电网企业对信息系统服务的有效管理?如何构建一套适合企业的信息系统客户服 ...
随机推荐
- Java实现 LeetCode 216. 组合总和 III(三)
216. 组合总和 III 找出所有相加之和为 n 的 k 个数的组合.组合中只允许含有 1 - 9 的正整数,并且每种组合中不存在重复的数字. 说明: 所有数字都是正整数. 解集不能包含重复的组合. ...
- Java 多线程基础(一)基本概念
Java 多线程基础(一)基本概念 一.并发与并行 1.并发:指两个或多个事件在同一个时间段内发生. 2.并行:指两个或多个事件在同一时刻发生(同时发生). 在操作系统中,安装了多个程序,并发指的是在 ...
- Python实现海贼王的歌词组成词云图
前言 本文的文字及图片来源于网络,仅供学习.交流使用,不具有任何商业用途,版权归原作者所有,如有问题请及时联系我们以作处理. 作者:一粒米饭 喜欢的朋友欢迎关注小编,除了分享技术文章之外还有很多福利, ...
- SQL--SQL详解(DDL,DML,DQL,DCL)
SQL--SQL详解(DDL,DML,DQL,DCL) 博客说明 文章所涉及的资料来自互联网整理和个人总结,意在于个人学习和经验汇总,如有什么地方侵权,请联系本人删除,谢谢! 什么是SQL? Stru ...
- refs转发 React.forwardRef
2020-04-01 refs转发 前几天刚总结完ref&DOM之间的关系,并且想通了3种ref的绑定方式 今天总结一下refs转发 这是react中一直困扰我的一个点 示例: 输入: wor ...
- Python多线程 - threading
目录 1. GIL 2. API 3. 创建子线程 4. 线程同步 4.1. 有了GIL,是否还需要同步? 4.1.1. 死锁 4.1.2. 竞争条件 4.1.3. GIL去哪儿了 4.2. Lock ...
- Ubuntu安装Vmware Tools解决屏幕比例失调
前言 安装ubuntu虚拟机时默认比例如下图,且ubuntu系统选项中没有合适的比例,可以安装Vmware Tools来解决. 注意:该方法只适用于有操作界面的系统,之前有位小伙伴在服务器上也想安装T ...
- Spring源码系列(一)--详解介绍bean组件
简介 spring-bean 组件是 IoC 的核心,我们可以通过BeanFactory来获取所需的对象,对象的实例化.属性装配和初始化都可以交给 spring 来管理. 针对 spring-bean ...
- Mybatis源码手记-从缓存体系看责任链派发模式与循环依赖企业级实践
一.缓存总览 Mybatis在设计上处处都有用到的缓存,而且Mybatis的缓存体系设计上遵循单一职责.开闭原则.高度解耦.及其精巧,充分的将缓存分层,其独到之处可以套用到很多类似的业务上.这里将主要 ...
- 获取ul下面最后一个li或ul中有多少个li
获取ul下面最后一个li或ul中有多少个li 先获取ul的对象,再通过这个对象获取li的list用for循环取值text之类的 def set_city(self, base_info): quali ...