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Enhancing Future Vehicles with AVR: Overcoming Bandwidth Challenges and Optimizing Localization

This paper explores the challenges and design considerations of using Augmented Vehicular Reality (AVR) to enhance vision in future vehicles. It delves into topics such as relative positioning, sparse features for localization, perspective transformation, bandwidth constraints, and extracting point clouds of moving dynamics. The study aims to reduce latency, improve bandwidth efficiency, and optimize the overall processing pipeline for AVR applications in vehicular environments.

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Enhancing Future Vehicles with AVR: Overcoming Bandwidth Challenges and Optimizing Localization

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  1. Augmented Vehicular Reality (AVR):Enabling Extended Vision for Future Vehicles Hang Qiu, Fawad Ahmad, Ramesh Govindan, Marco Gruteser, Fan Bai, Gorkem Kar

  2. Advanced Sensing in Cars LiDar Radar Stereo Camera

  3. 3D Perception: The Point Cloud . • Voxel • Position (x, y, z) • Color (R, G, B)

  4. Shadows Sensor limited by Line-of-sight

  5. 8

  6. AVRChallenges x d ? • Extremely Limited V2V bandwidth • Perspective Transformation • Relative positioning • Low Latency

  7. AVRDesign: Relative Positioning ?

  8. AVRDesign: Using Sparse Features for Localization Static Sparse Features

  9. AVRDesign: Collecting the 3D feature map

  10. AVRDesign: Localization using sparse 3D map y x

  11. AVRDesign: Perspective Transformation y x

  12. AVRDesign: The Bandwidth

  13. AVRDesign: The Bandwidth at 30fps ~15X

  14. AVRDesign: Dealing with the Bandwidth constraint • Isolating moving objects • Compressing point clouds of moving objects (Future Work)

  15. AVRDesign: Extracting Point Clouds of Dynamics • Find the homograph of the current frame in the previous frame. (x, y) * H = (x’, y’)

  16. AVRDesign: Extracting Point Clouds of Dynamics • Leveraging the 1-1 mapping between pixels and voxels to compute the voxel displacement. • Isolating the moving voxels from the point cloud.

  17. AVRPreliminaryResult Point Cloud of Leader Point Cloud of Follower Merged Point Cloud

  18. AVRFuture Work • Further reduce the latency and shrink the bandwidth needed. • Motion Prediction. • Dead-reckoning. • Advanced Point Cloud Compression. • Optimizing the processing pipeline • GPU utilization • Module pipeline • Explore better V2V communication possibilities • WiFi-direct, LTE • Distributed Mu-MIMO • DSRC • LTE-direct

  19. ? ? ? ? Thank You! ?

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