# Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter Source: [Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:24:11.561Z Authors: Hsu-kuang Chiu, Chien-Yi Wang, Min-Hung Chen, Stephen F. Smith Published: 2023-09-26T04:14:13.000Z ## Abstract Current state-of-the-art autonomous driving vehicles mainly rely on each individual sensor system to perform perception tasks. Such a framework's reliability could be limited by occlusion or sensor failure. To address this issue, more recent research proposes using vehicle-to-vehicle (V2V) communication to share perception information with others. However, most relevant works focus only on cooperative detection and leave cooperative tracking an underexplored research field. A few recent datasets, such as V2V4Real, provide 3D multi-object cooperative tracking benchmarks. However, their proposed methods mainly use cooperative detection results as input to a standard single-sensor Kalman Filter-based tracking algorithm. In their approach, the measurement uncertainty of different sensors from different connected autonomous vehicles (CAVs) may not be properly estimated to utilize the theoretical optimality property of Kalman Filter-based tracking algorithms. In this paper, we propose a novel 3D multi-object cooperative tracking algorithm for autonomous driving via a differentiable multi-sensor Kalman Filter. Our algorithm learns to estimate measurement uncertainty for each detection that can better utilize the theoretical property of Kalman Filter-based tracking methods. The experiment results show that our algorithm improves the tracking accuracy by 17% with only 0.037x communication costs compared with the state-of-the-art method in V2V4Real. Our code and videos are available at https://github.com/eddyhkchiu/DMSTrack/ and https://eddyhkchiu.github.io/dmstrack.github.io/ . ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Tracking on V2X-Seq](): 3D Object Tracking; V2X-Seq; AMOTA (0-150m Overall): 27.1. - [3D Object Detection on V2X-Seq](): 3D Object Detection; V2X-Seq; AP (0-150m): 22.4. - [3D Object Detection on Griffin 25m](): 3D Object Detection; Griffin 25m; AP (Overall, 0-100m): 13.6. - [Multi-Object Tracking on Griffin 25m](): Multi-Object Tracking; Griffin 25m; AMOTA (0-100 m Overall): 9.9. ## Original sources and implementation [Original paper]() [PDF]()