badas 1.1.0

Beyond ADAS — collision anticipation inference for dashcam video

BADAS - Beyond ADAS

BADAS (Beyond ADAS) is a deep learning framework for predicting collision likelihood in dashcam video sequences. It supports multiple vision transformer backbones and deployment formats optimized for real-time inference.

Installation

pip install badas

Optional extras for faster video decoding or alternative backends:

pip install decord           # Fast video decoding (falls back to OpenCV)
pip install onnxruntime-gpu  # ONNX backend

Quick Start

from badas.inference import BADAS, BADASConfig

predictor = BADAS.from_pretrained("nexar-ai/BADAS-Open")

# Batch prediction on a video
results = predictor.predict_video("dashcam.mp4", stride=1)
for r in results:
    print(f"[{r['timestamp']:.2f}s]  {r['risk_level']:<6}  p={r['probability']:.3f}")

# Streaming / moving-window inference
for pred in predictor.predict_stream("dashcam.mp4", stride=1):
    if pred['probability'] > 0.7:
        print(f"WARNING: High collision risk at {pred['timestamp']:.2f}s")

Prediction Output

Each prediction is a dict:

{
    'frame_index': 100,
    'timestamp': 4.17,
    'probability': 0.823,
    'risk_level': 'high',   # 'low' | 'medium' | 'high'
    'smoothed': True,
}

Inference Backends

Format	Load	Notes
.ckpt / .pt	BADAS("model.ckpt")	Full model + config, supports torch.compile
.onnx	BADAS("model.onnx")	CPU/GPU via ONNX Runtime
.trt / .engine	BADAS("model.trt")	NVIDIA GPU, lowest latency

Configuration

from badas.inference import BADAS, BADASConfig, SmoothingConfig

config = BADASConfig(
    use_compile=True,       # torch.compile — slow first run, fast thereafter
    startup_ramp=True,      # yield predictions before the full window is buffered
    smoothing=SmoothingConfig(
        enabled=True,
        alpha_rise=0.7,     # smoothing when risk increases
        alpha_fall=0.3,     # smoothing when risk decreases
    ),
)
predictor = BADAS("model.ckpt", config=config)

Available Models

Open Source

Model	Size	Notes
nexar-ai/BADAS-Open	ViT-L	Publicly available

predictor = BADAS.from_pretrained("nexar-ai/BADAS-Open")

Commercial (Nexar)

Model	Size	Notes
nexar-ai/badas-1.0	ViT-L
nexar-ai/badas-1.5	ViT-L	Best accuracy
nexar-ai/badas-1.5-flash	ViT-B	Fast
nexar-ai/badas-1.5-flash-lite	ViT-S	Fastest

To request access, visit nexar-ai.com/badas.

Citation

@article{goldshmidt2025badas,
  title={BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data},
  author={Goldshmidt, Roni and Scott, Hamish and Niccolini, Lorenzo and
          Zhu, Shizhan and Moura, Daniel and Zvitia, Orly},
  journal={arXiv preprint},
  year={2025}
}