OFEODM: An Vehicle Onboard Fast and Efficient Object Detection and Distance Prediction Model University of Oklahoma

Format:

Book

Conference/Event

Author/Creator:

Li, Taozhe, author.

Wang, Hanchen, author.

Hajnorouzali, Yasaman, author.

Xu, Bin, author.

Conference Name:

WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)

Language:

English

Subjects (All):

Autonomous vehicles.

Electric vehicles.

Trajectory control.

Design processes.

Frames.

Local Subjects:

Autonomous vehicles.

Electric vehicles.

Trajectory control.

Design processes.

Frames.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Object detection and distance prediction have advanced significantly in recent years. The YOLO toolbox has released its 11th version, along with numerous variants that have been applied across various fields. Meanwhile, the Detection Transformer (DETRs) has repeatedly set new state-of-the-art (SOTA) records in the field of object detection. Depth Anything also released its second version last year, further pushing the boundaries of distance detection. Although these models achieve impressive performance, they often require substantial computational resources. However, for the algorithms intended for real-world applications and deployment on onboard devices, computational efficiency are extremely critical. Inference time per frame is a critical factor in ensuring an algorithm's reliability and feasibility. Designing a model that operates in real time without sacrificing accuracy remains an extremely challenging problem, and extensive research is ongoing in this area. To address this challenge, we present a model called the Fast Detection model, which runs in real time on a comma 3X device equipped with a Qualcomm Snapdragon 845 processor. We deployed the comma 3X device on a 2025 Nissan Leaf electric vehicle for autonomous driving purposes. Furthermore, experimental results of comparing our model to the state-of-the-art one-stage object detection models of the YOLO series indicate that our model demonstrates comparable performance but with faster speed on our collected real-world dataset. Additionally, we have incorporated an extra module into our Fast Detection model that enables it to predict the distance between our vehicle and detected objects, providing valuable information for downstream tasks such as path planning

Notes:

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Access Restriction:

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