V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
arXiv:2008.07519
Abstract
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently aggregating the information received from multiple nearby vehicles, we can observe the same scene from different viewpoints. This allows us to see through occlusions and detect actors at long range, where the observations are very sparse or non-existent. We also show that our approach of sending compressed deep feature map activations achieves high accuracy while satisfying communication bandwidth requirements.
ECCV 2020 (Oral)
References in corpus (7)
- Deep Continuous Fusion for Multi-Sensor 3D Object Detection
- Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
- IntentNet: Learning to Predict Intention from Raw Sensor Data
- MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
- Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
- Situation Recognition with Graph Neural Networks
- VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach