# BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving

arXiv: https://arxiv.org/abs/1903.06405 · HTML: https://mlsanity.com/paper/1903.06405/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 13 of 13)

- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [PCGen: Point Cloud Generator for LiDAR Simulation](https://mlsanity.com/paper/2210.08738/) (2022)
- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Staged Contact-Aware Global Human Motion Forecasting](https://mlsanity.com/paper/2309.08947/) (2023)
- [Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics](https://mlsanity.com/paper/2303.04302/) (2023)
- [A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN](https://mlsanity.com/paper/2103.00801/) (2021)
- [All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles](https://mlsanity.com/paper/2510.26641/) (2025)
- [An End-to-End Vehicle Trajcetory Prediction Framework](https://mlsanity.com/paper/2304.09764/) (2023)
- [Vehicle Behavior Prediction by Episodic-Memory Implanted NDT](https://mlsanity.com/paper/2402.08423/) (2024)
- [EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving](https://mlsanity.com/paper/2502.19260/) (2025)

## Abstract (arXiv)

In autonomous driving community, numerous benchmarks have been established to assist the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation. However, the more meaningful dynamic evolution of the surrounding objects of ego-vehicle is rarely exploited, and lacks a large-scale dataset platform. To address this, we introduce BLVD, a large-scale 5D semantics benchmark which does not concentrate on the static detection or semantic/instance segmentation tasks tackled adequately before. Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction. This benchmark will boost the deeper understanding of traffic scenes than ever before. We totally yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime). The benchmark can be downloaded from our project site https://github.com/VCCIV/BLVD/.