README.md

metadata

pretty_name: EventDrive
language:
  - en

EventDrive

EventDrive: Event Cameras for Vision-Language Driving Intelligence

Dongyue Lu, Rong Li, Ao Liang, Lingdong Kong, Wei Yin, Lai Xing Ng, Benoit R. Cottereau, Camille Simon Chane, and Wei Tsang Ooi

CVPR 2026

Project Page | Paper | Dataset

EventDrive is a unified event-frame driving benchmark for vision-language driving intelligence. It combines synchronized RGB frames, event-camera data, and instruction-style annotations to study how event sensing supports multimodal perception, reasoning, prediction, and planning under diverse driving conditions.

The benchmark covers four dimensions:

• Perception: scene-level driving perception questions.
• Understanding: object awareness, grounding, appearance, status, and spatial-relation questions.
• Prediction: short-term behavior prediction for a highlighted dynamic agent.
• Planning: high-level driving intent and ego-trajectory prediction.

Repository Layout

.
├── eventdrive_perception.tar.gz
├── eventdrive_understanding.tar.gz
├── eventdrive_prediction.tar.gz
├── eventdrive_planning.tar.gz
├── json/
│   ├── perception/
│   │   ├── dsec/
│   │   ├── m3ed/
│   │   └── pku/
│   ├── understanding/
│   ├── prediction/
│   └── planning/
└── scripts/
    ├── evaluation_perception.py
    ├── evaluation_understanding.py
    ├── evaluation_prediction.py
    └── evaluation_planning.py

Create a data/ directory and extract all archives from the repository root:

mkdir -p data
tar -xzf eventdrive_perception.tar.gz -C data
tar -xzf eventdrive_understanding.tar.gz -C data
tar -xzf eventdrive_prediction.tar.gz -C data
tar -xzf eventdrive_planning.tar.gz -C data

The extracted data follows this structure:

data/
├── perception/
│   ├── dsec/
│   │   ├── train/<sequence>/{image,event}/
│   │   └── test/<sequence>/{image,event}/
│   ├── m3ed/<sequence>/{image,event}/
│   └── pku/aps_frames_sampled/val/<condition>/<sequence>/
├── understanding/
│   ├── train/<sequence>/{image,event}/
│   └── test/<sequence>/{image,event}/
├── prediction/<sequence>/{image,event}/
└── planning/<sequence>/{image,event}/

Each image/ directory contains RGB frames. Each event/ directory contains the paired event-camera representation in .npz format.

For PKU perception data, paired .png and .npz files are stored side by side in each sequence directory instead of separate image/ and event/ directories.

Annotation Files

All annotation paths are relative to the repository root and start with data/. Run the scripts from the repository root after extracting the archives.

Each dimension provides train and test annotations. Files ending in _hard.json contain the hard test subsets.

The released annotations under json/ use separate prompts for answer components such as option letter and label text, or speed and path intent. Samples originating from the same question share an original_id. The evaluation scripts use this field to pair component predictions before computing joint accuracy. Planning trajectory samples are evaluated independently and do not require an original_id.

A typical annotation includes paired image and event paths plus an instruction-answer conversation:

{
  "image": "data/perception/dsec/test/interlaken_00_a/image/000005.png",
  "event": "data/perception/dsec/test/interlaken_00_a/event/000005.npz",
  "category": "Scene type",
  "original_id": "perception/dsec/dsec_test_perception.json:000000",
  "subtask": "option_letter",
  "conversations": [
    {
      "from": "human",
      "value": "<instruction>"
    },
    {
      "from": "gpt",
      "value": "<ground-truth answer>"
    }
  ]
}

Add a model_output field to each sample after inference:

{
  "model_output": "<model prediction>"
}

For understanding grounding samples, boxes use [x, y, w, h], where (x, y) is the top-left corner and (w, h) is the width and height.

Evaluation

Install the evaluation dependencies:

pip install numpy tqdm

Run the matching evaluator on an inference result JSON file generated from the annotations under json/:

python scripts/evaluation_perception.py \
  --pred-json results/dsec_test_perception.json

python scripts/evaluation_understanding.py \
  --pred-json results/dsec_test_understanding.json \
  --iou-thresh 0.6

python scripts/evaluation_prediction.py \
  --pred-json results/m3ed_test_prediction.json

python scripts/evaluation_planning.py \
  --pred-json results/m3ed_test_planning.json

The evaluators write summary JSON files next to the prediction file. They also save mismatch examples for debugging when applicable.

Metrics:

• Perception: joint accuracy after pairing the split option-letter and label-text answers. Both answers must be correct.
• Understanding: joint QA accuracy after pairing the split option-letter and label-text answers, category-wise accuracy, grounding accuracy at the selected IoU threshold, and mean IoU. Both QA answers must be correct. The default IoU threshold is 0.6.
• Prediction: speed accuracy, path accuracy, class-wise accuracy, and joint speed-path accuracy after pairing split answers.
• Planning: high-level speed accuracy, path accuracy, class-wise accuracy, joint speed-path accuracy after pairing split answers, and trajectory L2 error at 1s, 3s, and 5s.

Planning trajectory predictions must contain exactly 10 [x, y] waypoints at 0.5-second intervals. Evaluation terminates with an error if a trajectory prediction does not follow this format.

Citation

@InProceedings{Lu_2026_CVPR,
  author    = {Lu, Dongyue and Li, Rong and Liang, Ao and Kong, Lingdong and Yin, Wei and Ng, Lai Xing and Cottereau, Benoit R. and Chane, Camille Simon and Ooi, Wei Tsang},
  title     = {EventDrive: Event Cameras for Vision-Language Driving Intelligence},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026},
}