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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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Collaborative Joint Perception and Prediction for Autonomous Driving.

Shunli Ren1, Siheng Chen1, Wenjun Zhang1

  • 1Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai 200240, China.

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|October 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces CoPnP, a new system for connected autonomous vehicles and roadside units to share multi-frame spatial-temporal data. CoPnP significantly improves joint perception and prediction performance for enhanced road safety.

Keywords:
autonomous drivingcollaborative perceptioninformation fusionjoint perception and predictionmulti-agent systemperformance-communication trade-offspatial–temporal information sharing

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Connected autonomous vehicles and roadside units enhance driving via information exchange.
  • Current methods limit temporal data sharing, hindering broader applications.

Purpose of the Study:

  • To propose CoPnP, a novel collaborative joint perception and prediction system.
  • To enable effective multi-frame spatial-temporal information sharing.

Main Methods:

  • Developed a task-oriented spatial-temporal information-refinement model to filter features.
  • Introduced a spatial-temporal importance-aware feature-fusion model for comprehensive data fusion.

Main Results:

  • CoPnP enhances collaboration benefits for joint perception and prediction.
  • Achieved significant performance-communication trade-offs on OPV2V/V2XSet datasets.
  • Demonstrated Intersection over Union and Video Panoptic Quality gains over single-agent systems.

Conclusions:

  • CoPnP advances collaborative perception and prediction for intelligent transportation systems.
  • The proposed models enable efficient and effective spatial-temporal information sharing.