arXiv AI September 12, 2026 By M\'ark Mez\H{o}-Kerekes, P\'eter Praksz, Chang Liu

Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless

The paper introduces a lightweight LiDAR-only perception pipeline for Formula Student Driverless vehicles that runs entirely on CPU. It combines ground removal, IMU-based motion compensation, DBSCAN clustering, and a Random Forest classifier, reducing the feature set from 12 to 7 while maintaining high accuracy. On a dataset of 2,371 labeled clusters, the system achieves an F1-score of 98.33% with an end-to-end runtime of 3.13 ms.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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arXiv AI

Jun 3

Towards Compact Autonomous Driving Perception with Balanced Learning and Multi-sensor Fusion

arXiv:2606. 02979v1 Announce Type: cross Abstract: We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass.

By Oskar Natan, Jun Miura

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arXiv Machine Learning

Aug 10

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By Niclas Meyer, Stefan Reitmann

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arXiv AI

Jun 9

Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic

arXiv:2606. 07626v1 Announce Type: cross Abstract: Perception in dense, unstructured urban traffic remains a major challenge for autonomous driving because of the wide variety of road users, frequent occlusions, irregular motion patterns, and the lack of standardized road layouts.

By Pranav Darshan, Raghuveer Narayanan Rajesh, M Uttara Kumari

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arXiv Machine Learning

Jul 10

On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection

arXiv:2607. 08391v1 Announce Type: cross Abstract: Making tradeoffs between execution latency and result utility (i.

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arXiv Machine Learning

Jun 9

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arXiv:2606. 08206v1 Announce Type: cross Abstract: We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds.

By Maciej Wielgosz, Stefano Puliti, Rasmus Astrup

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arXiv Computer Vision

Sep 1

Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation

arXiv:2409.11018v3 Announce Type: replace Abstract: The LiDAR 3D object detector that balances accuracy and speed is crucial for achieving real-time perception in autonomous driving. However, many ex...

By Rui Yu, Runkai Zhao, Jiagen Li, Qingsong Zhao, HuaiCheng Yan, Meng Wang

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