UniSense: Spatial-Uncertainty-Aware Collaborative Sensing for Autonomous Driving

Haojie Ren, Wuyang Zhang, Shuyao Shi, Xinran Zhang, Lu Zhang, Yanyong Zhang

Abstract: Vehicle-to-vehicle collaborative perception faces fundamental deployment barriers: raw LiDAR data sharing requires over 300 Mbps per vehicle - far exceeding V2X network capacities, while network delays of 80-200ms create dangerous temporal misalignments at highway speeds. We present UniSense, a distributed collaborative perception system that enables efficient and reliable multi-vehicle perception through uncertainty-driven sensor data exchange. Instead of sharing raw sensor data, vehicles exchange compact uncertainty maps that identify regions requiring additional perceptual information. Our key innovations include: (1) a lightweight uncertainty quantification pipeline that runs in real-time on automotive hardware, identifying perception-critical regions while reducing bandwidth requirements by more than 10×, (2) a bandwidth-aware protocol that dynamically adapts data sharing based on network conditions and perception uncertainty, and (3) a selective motion compensation scheme that maintains temporal consistency. We evaluate UniSense through a year-long deployment with 16 roadside LiDAR nodes and autonomous vehicles across our campus. Our experimental results show that UniSense extends reliable perception range from local 80m to 140m, improving accuracy by 1.33× on average, up to 1.73×, over the state-of-the-art baselines, under communication constraints. The code and dataset are available at https://github.com/LetStarFly/UniSense.

External IDs:doi:10.1145/3711875.3729130