Amateur: Augmented Reality Based Vehicle Navigation System Chu Cao 1 , Zhenjiang Li 2 , Pengfei Zhou 1 , Mo Li 1 Nanyang Technological University 1 , Singapore City University of Hong Kong 2 , Hong Kong, China 12 September 2019 UbiComp’19, London, UK
� 2 Transportation System Vehicular navigation system Mobile navigation service
� 3 Transportation System Problem: display digital map Gap: real v.s. virtual
� 4 AR-based Navigation Service ✦ Display front-view road condition ✦ Instructions on live world ✦ Comparable navigation ✦ Easy to deploy
� 5 AR-based Navigation Service ❖ Challenges Determine the correct instruction at proper position on screen. ✦ Host-lane identifying ✦ Annotations placement Lane-level localisation accuracy Depth information in video
� 6 AR-based Navigation Service ❖ Related work CarLoc: Precisely Tracking Automobile Real time Detection of Lane Markers in Position [SenSys’15] Urban Streets [IVS’08] 20 built-in sensors Complicated image processing ✦ ✦ Dead-reckoning Too heavy to be affordable ✦ ✦
� 7 AR-based Navigation Service ❖ Related work Tesla autopilot 2.0 Google automobile Maintain on hostlane Rich sensor embedded ✦ ✦
� 8 AR-based Navigation Service ❖ Related work Towards Unified Depth and Semantic Prediction from a Single Image [CVPR’15] Three complicated neural networks ✦ Large volume of training data ✦
� 9 AR-based Navigation Service ❖ System architecture Instructional Sign Placement Lane-Road Information Instruction Type Instruction Position AR Result Host Lane Intersection Navigation Particle Filter GPS Pin-hole Model & Live Motion Origin and Destination Peak Detection Traffic Light Detection video sensor System GPS User Input Frame Slice Subtracter Frames Lane Identification Intersection Inference ce
� 10 AR-based Navigation Service ❖ System architecture Instructional Sign Placement Lane-Road Information Instruction Type Instruction Position AR Result Host Lane Intersection Navigation Particle Filter GPS Pin-hole Model & Live Motion Origin and Destination Peak Detection Traffic Light Detection video sensor GPS User Input Frame Slice Subtracter Frames Lane Identification Intersection Inference
� 11 AR-based Navigation Service ❖ System architecture — lane identification Lane detection task. Lane identification task. Based on pure videos. Based not only on videos… IMU sensors on mobile phone & Avoid collisions for automobiles. Extra lane number information Assistant for drivers.
� 12 AR-based Navigation Service ❖ System architecture — lane identification One frame in video Image slicing of 60 frames
<latexit sha1_base64="SZ0J2yGuG58XxklAN9wP4PnaU=">ACB3icdVDLSsNAFJ34rPUVdSnIYBEsSRtoXZXcKHLCvYBbQiT6aQdOnk4MxFKyM6NfoA/4M6NC0Xc+gvu/AD/w2lSQUPXDicy/3uOEjApGO/azOzc/MJibim/vLK6tq5vbLZEHFMmjhgAe84SBGfdKUVDLSCTlBnsNI2xkdT/z2JeGCBv65HIfE8tDApy7FSCrJ1nd6Lkc4NpO4nISwx8gFjMPDEzs2DTlMElsvGMVaCpiRamVKaiY0i0aKQv3grnX7cVNu2Ppbrx/gyCO+xAwJ0TWNUFox4pJiRpJ8LxIkRHiEBqSrqI8Iqw4/SOBe0rpQzfgqnwJU/X7RIw8Icaeozo9JIfitzcR/K6kXSPrJj6YSJj7NFbsSgDOAkFNinGDJxogzKm6FeIhUsFIFV1ehfD1KfyftEpFs1wsnak0KiBDmyDXbAPTFAFdXAKGqAJMLgC9+ARPGnX2oP2rL1krTPadGYL/ID2+gnkFp0C</latexit> <latexit sha1_base64="9RDv2MYFcOXvS2/hoKTrS5Q6S04=">ACDHicdVDLSgMxFM34rPVdekmWARBKDNtoXYhFNy4rGAf0Kklk2ba0MzDJCOWMB+gC5f+gB/gxoUibv0Ad36A/2FmWkFDwQO5zLzT1OyKiQpvluzMzOzS8sZpayura+u5jc2mCKOSQMHLOBtBwnCqE8akpG2iEnyHMYaTmjo8RvXRAuaOCfynFIuh4a+NSlGEkt9XL5oT0g59B2OcLKilUx9tClrQY9asc9RQ+t+CxJmYVqCjghlfKUVC1oFcwU+dr+XfP247pU7+Xe7H6AI4/4EjMkRMcyQ9lViEuKGYmzdiRIiPAIDUhHUx95RHRVekwMd7XSh27A9fMlTNXvEwp5Qow9Ryc9JIfit5eIf3mdSLoHXUX9MJLEx5NFbsSgDGDSDOxTrBkY0Q5lT/FeIh0r1I3V9Wl/B1KfyfNIsFq1Qonug2ymCDNgGO2APWKACauAY1EDYHAF7sEjeDJujAfj2XiZRGeM6cwW+AHj9ROtdJ+1</latexit> <latexit sha1_base64="Rkf7FqIuQSgMdOn9OZY+TVWMXbI=">ACG3icdZDLSgMxFIYz9VbrerSTVBEQVpmxkLtQi40GUF2yqdWjJpg3NZIYko5Rh3sONj6IbF4q4ElwIfRjTi2BFfwj8fOcTs7vhoxKZqfRmpmdm5+Ib2YWVpeWV3Lrm/UZBAJTKo4YIG4dJEkjHJSVQxchkKgnyXkbrbOxnW6zdESBrwC9UPSdNHU49ipHSqJW1w+NuzqfciTst6iTXce6d+B4AuH4NontJGkNSW6KZHfMfGkODbFwsSULGjlzZF2yvtXp3uDh0GlX132gGOfMIVZkjKhmWGqhkjoShmJMk4kSQhwj3UIQ1tOfKJbMaj2xK4q0kbeoHQjys4oj8nYuRL2fd3ekj1ZW/a0P4V60RKe+oGVMeRopwPF7kRQyqA6Dgm0qCFasrw3Cguq/QtxFOgal48zoEL4vhf+bmp23DvP2uU6jAMZKgy2wDfaBYqgDM5ABVQBnfgETyDF+PeDJejbdxa8qYzGyCKRkfX4oprQ=</latexit> � 13 AR-based Navigation Service ❖ System architecture — lane identification 4 200 5 2 3 6 1 150 Grey value h p 100 7 50 w 0 0 200 400 600 800 1000 1200 1400 1600 1800 Pixel indicator 1 h ≥ 1 g k + w 2 max { g i } n p = h − min { g i } 2 3 p ≤ p − G 10 th g k − w i =1 2 Brightness Sharpness
� 14 AR-based Navigation Service ❖ System architecture — lane identification Lane Marker [790] [1106] [474] Number X 1 [-474] [-474] [474] [474] [790] [790] X 2 Lane [474] [-790] [-474] Direction X 3 [- [-1106] [-790] [-474] X 4 Lane Number A road segment with 4 lanes Templates of a 4-lane road
� 15 AR-based Navigation Service ❖ System architecture — lane identification X obs X 3 [-790] [-474] [474] [474] [-790] [-474] Matching Ideally detect the peaks 1. Blockage of frontal vehicles 2. Reflection of lights 3. Bad condition of lane markers
� 16 AR-based Navigation Service ❖ System architecture — lane identification ✦ Particle filter design [474] [790] [1106] X obs X 1 [-790] [-474] [474] [790] [790] [-474] [474] [-474] [474] [790] X 2 [-790] [-474] [474] Weight updating X 3 [-1106] [-790] [-474] X 4 Dynamic time wrapping initialisation
<latexit sha1_base64="eXOYNe48FvqEsizy0aM4VtsE/i0=">AB9XicdVDLSsNAFJ3UV62vqhvBzWAR3BiSWqhFhIblxXsA9o0TCaTdujkwczEUkL3foIbF4q4danf4c6/cZpUNEDFw7n3Mu9zgRo0IaxoeW1hcWl7JrxbW1jc2t4rbOy0RxhyTJg5ZyDsOEoTRgDQlYx0Ik6Q7zDSdkYXM79Q7igYXAtJxGxfDQIqEcxkrqj2nH52TfnLs2s7ULpYMvZYCZqRamZOaCU3dSFGq791ar6Ozt4ZdfO+5IY59EkjMkBd04iklSAuKWZkWujFgkQIj9CAdBUNkE+ElaRXT+GhUlzohVxVIGqfp9IkC/ExHdUp4/kUPz2ZuJfXjeW3qmV0CKJQlwtsiLGZQhnEUAXcoJlmyiCMKcqlshHiKOsFRBFVQIX5/C/0mrJsnevlKpVEBGfJgHxyAI2CKqiDS9ATYAB3fgATxqY+1e9Kes9acNp/ZBT+gvXwCiCWLQ=</latexit> � 17 AR-based Navigation Service ❖ System architecture — lane identification ✦ Particle filter design Dynamic time wrapping under Euclidean distance X obs X 3 [-790] [-474] [474] [790] [474] [-790] [-474] Matching w p b = e − d b
� 18 AR-based Navigation Service ❖ System architecture — lane identification ✦ Particle filter design Resampling based on importance X obs [474] [790] [-790] [-474] Movement: lane switching
� 19 AR-based Navigation Service ❖ System architecture — lane identification ✦ Particle filter design Resampling based on importance
� 20 AR-based Navigation Service ❖ System architecture — lane identification ✦ Particle filter design Resampling based on importance Lane marker of host-lane has a traversing phenomenon during lane switching.
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