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A SOTA World Model for Risk Anticipation

BADAS 2.0
The world's best incident prediction model, period.

BADAS 2.0 is a world model that has internalized physics and causality. We trained it on 2M real-world driving clips. We fine-tuned NVIDIA's COSMOS on the same data. BADAS wins – at 91x fewer parameters. We publish everything. Try it yourself.

99.4% Average Precision
91% Early Warning
4.6% False Alarm Rate
Cloud to Edge
Try BADAS 2.0 →See the Benchmarks
01 – Live Demo
Run BADAS 2.0 on Your Own Video

Watch the world's best incident prediction and understanding model in action.

Upload Your Own Video. See What BADAS 2.0 Sees.

Drag an EGO POV video. Get predictions, attention heatmaps, and natural language reasoning – in seconds.

Choose Video File
Supported: MP4, MOV, AVI – up to 60 seconds
Real Incidents
Predictions
vs BADAS 1.0
vs Cosmos
Heatmap
Reasoning
Real Incident 1
Real Incident 2
Real Incident 3
Real anonymized edge device incidents – BADAS 2.0 prediction overlay on actual near-collision events from Nexar's 350K camera fleet
Bumper Cars
Formula 1
BADAS 2.0 collision probability overlay -- probability ramps from near-zero as danger develops.
Bumper Cars
Formula 1
Generational leap - BADAS 2.0 detects danger earlier with higher confidence and 58% fewer false positives
Bumper Cars
Formula 1
BADAS 2.0 vs Cosmos-BADAS - purpose-built vision transformer outperforms fine-tuned VLM on temporal discrimination
Bumper Cars
Formula 1
Attention heatmap - visualizes where the model focuses as danger develops
Bumper Cars
Formula 1
BADAS-Reason - natural language explanations describe what the model sees and why it predicts danger"

A New Standard for Road Safety

02 – The Open Challenge

0.994 AP vs 0.940. Same Data.
Different Architecture.

We fine-tuned NVIDIA's COSMOS-Reason2-2B on the exact same 2M training clips. BADAS 2.0 achieves 99.4% AP. COSMOS achieves 94.0%. At 91x fewer parameters, our smallest model still beats their largest. We publish our benchmarks and invite direct comparison.

Metric
BADAS 2.0
Cosmos-BADAS
Average Precision
99.4%
94.0%
Early Warning Recall
91.3%
48.3%
Architecture
V-JEPA2 (Attention)
Autoregressive
Smallest Model
22M params
2B params (cloud only)
Training Data
2M real-world clips
2M real-world clips (same)
Explainability
Native attention maps
None

COSMOS-BADAS = NVIDIA COSMOS-Reason2-2B fine-tuned on the same 2M Nexar training clips used by BADAS 2.0.

The Efficiency Gap: Fine-tuning improved COSMOS by +18.5 pp AP – but at 2B parameters, it still sits 5.4 pp below BADAS 2.0 (94.0% vs 99.4%). BADAS 2.0 Flash Lite (22M) outperforms COSMOS-BADAS (2,000M) by +4.4 pp AP while being 91x smaller.
Early Warning Recall
48.3%
COSMOS-BADAS
91.3%
BADAS 2.0
In collision anticipation, late is too late.
The Efficiency Gap
BADAS 2.0 Flash Lite (22M) outperforms COSMOS-BADAS (2,000M) – 91× smaller.
BADAS 2.0 — 300M, 99.4% AP
BADAS 1.0 — 300M, 96.0% AP
BADAS 2.0 Flash — 86M, 99.0% AP
BADAS 2.0 Flash Lite — 22M, 98.4% AP
COSMOS-BADAS — 2B, 94.0% AP
COSMOS-Reason2 — 2B, 75.6% AP
03 – The Proof

We Published The Benchmarks. Your Move.

Long-Tail Benchmark: Per-Category Breakdown

AUC and AP across 10 scenario groups (888 clips, sliding window). BADAS 2.0 leads in every category. Best per row in bold.

Group
Metric
BADAS
2.0
BADAS
2.0
Flash
BADAS
2.0
Flash Lite
BADAS
1.0
BADAS
Open
COSMOS-
Reason2
Fine-tuned
Gemini 2.5
Pro
Fine-tuned
Qwen3-
VL-2B
Animal
AUC
96.4%
94.8%
92.3%
94.8%
88.1%
81.6%
79.9%
75.9%
AP
99.1%
98.8%
98.1%
98.7%
95.7%
95.2%
93.9%
93.2%
AUC
99.8%
99.6%
99.1%
99.1%
Intersection
84.4%
93.6%
77.4%
67.4%
AP
99.9%
99.7%
99.4%
99.4%
87.9%
95.6%
77.5%
75.5%
AUC
100.0%
100.0%
99.8%
98.8%
95.8%
97.9%
85.0%
61.7%
AP
100.0%
100.0%
99.7%
98.4%
93.7%
96.9%
73.7%
45.3%
AUC
100.0%
100.0%
99.7%
97.4%
92.7%
97.5%
83.2%
82.6%
AP
100.0%
99.9%
99.4%
96.1%
Overtaking
85.3%
94.8%
60.8%
68.0%
AUC
100.0%
100.0%
99.9%
99.6%
93.2%
97.5%
95.3%
80.4%
AP
100.0%
100.0%
99.9%
99.5%
94.0%
97.3%
93.6%
77.9%
Snow
AUC
100.0%
99.4%
98.4%
86.9%
84.0%
97.4%
90.8%
58.4%
AP
100.0%
99.6%
98.7%
91.5%
88.0%
98.1%
92.2%
62.5%
AUC
99.8%
99.8%
100.0%
99.8%
Infrastructure
96.6%
98.9%
80.1%
72.5%
AP
99.9%
99.9%
100.0%
99.9%
96.9%
99.1%
76.6%
76.1%
AUC
100.0%
98.7%
99.4%
98.6%
93.1%
94.0%
82.9%
64.8%
Motorcyclist
AP
100.0%
99.1%
99.5%
98.7%
94.2%
95.3%
81.2%
59.8%
AUC
100.0%
100.0%
100.0%
97.5%
96.6%
99.6%
82.2%
81.5%
AP
100.0%
100.0%
100.0%
98.0%
Cyclist
95.9%
99.4%
64.5%
66.2%
AUC
100.0%
99.8%
99.8%
100.0%
99.4%
98.2%
81.6%
82.5%
AP
100.0%
99.5%
99.5%
100.0%
98.7%
95.9%
60.5%
76.0%
Rain
AUC
99.3%
98.9%
98.1%
94.9%
82.3%
92.6%
83.3%
67.8%
AP
99.4%
99.0%
98.4%
96.0%
84.5%
94.1%
79.5%
0.672
Fog
OVERALL
Pedestrian
Nexar Kaggle Benchmark

Single-window mean AP over three lead-time thresholds (1,344 clips). BADAS 2.0 improves mAP from 0.925 to 0.940 while cutting the false positive rate by 74%.

Model
AP @0.5s
AP @1.0s
AP @1.5s
mAP
FPR
Params
BADAS 2.0
0.943
95.7%
92.1%
94.0%
4.6%
300M
BADAS 2.0 Flash
0.945
96.2%
91.5%
94.1%
9.7%
86M
BADAS 2.0 Flash Lite
0.946
94.7%
90.7%
93.3%
12.2%
22M
BADAS 1.0
93.5%
93.6%
90.4%
92.5%
10.9%
300M
COSMOS-BADAS
90.4%
88.9%
87.5%
88.9%
–
2B
Reading this table: AP @0.5s / @1.0s / @1.5s – Average Precision measured at three different lead times before the collision. Higher = better detection at that warning horizon. mAP – Mean Average Precision, the average of the three AP scores above. FPR – False Positive Rate. Lower = fewer false alarms. Params – Model size in parameters.
74% Fewer False Alarms

On the internal test set, BADAS 2.0 cuts the false positive rate from 17.7% (v1.0) to 4.6% – a 74% reduction with no loss of recall.

4.6%
FPR – BADAS 2.0
mAP 94.0% · 300M params · 34ms
9.7%
FPR – BADAS 2.0 Flash
mAP 94.1% · 86M params · 4.8ms
10.9%
FPR – BADAS 1.0
mAP 92.5% · 300M params · 2,500ms
Early Warning Recall (Long-Tail Benchmark)

Fraction of collision events detected before they occur (888 clips, 10 scenario groups, threshold 0.75).

BADAS 2.0
91.3%
F1 96.4%
BADAS 2.0 Flash
89.9%
F1 93.8%
BADAS 1.0
85.5%
F1 87.6%
External Benchmarks (Sliding Window)

AUC and AP on three public academic benchmarks using ego-centric re-annotation. Best per column in bold.

Model
DAD AUC
DAD AP
DoTA AUC
DoTA AP
DADA AUC
DADA AP
BADAS 2.0
99.3%
92.2%
99.1%
99.9%
99.1%
99.6%
BADAS 2.0 Flash
98.7%
84.9%
98.5%
99.8%
99.0%
99.5%
BADAS 2.0 Flash Lite
98.2%
87.0%
98.5%
99.8%
98.1%
99.2%
BADAS 1.0
99.0%
94.0%
72.0%
95.0%
87.0%
90.0%
COSMOS-BADAS
94.4%
60.2%
98.3%
99.8%
95.9%
97.8%
Qwen3-VL-2B
75.4%
14.1%
70.9%
95.1%
80.5%
88.6%
Reading this table: AUC – Area Under the ROC Curve. Measures how well the model separates collisions from safe driving. 100% = perfect. AP – Average Precision. DAD, DoTA, DADA-2000 – Three public academic collision anticipation benchmarks with re-annotated ego-centric protocol.
How Confidence Evolves Over Time

Average collision probability over normalized pre-event time (0% = start, 100% = event). Each clip's timeline is scaled independently, so clips of different lengths are comparable. Positive clips only. BADAS models ramp up sharply; competitors stay flat.

Kaggle
Long-tail
DAD
DoTA
DADA-2000
OOD Test
BADAS 2.0
BADAS 2.0 Flash
BADAS 2.0 Flash Lite
BADAS 1.0
BADAS Open
COSMOS-BADAS
Gemini 2.5 Pro Tuned
Qwen3-VL-2B
COSMOS-Reason2
BADAS 2.0
BADAS 1.0
BADAS 2.0 Flash
BADAS Open
Cosmos-BADAS
Gemini 2.5 Pro Tuned
Qwen3-VL-2B
Cosmos Vanilla
BADAS 2.0
BADAS 1.0
BADAS 2.0 Flash
BADAS Open
Cosmos-BADAS
Gemini 2.5 Pro Tuned
Qwen3-VL-2B
Cosmos Vanilla
BADAS 2.0
BADAS 1.0
BADAS 2.0 Flash
BADAS Open
Cosmos-BADAS
Gemini 2.5 Pro Tuned
Qwen3-VL-2B
Cosmos Vanilla
BADAS 2.0
BADAS 1.0
BADAS 2.0 Flash
BADAS Open
Cosmos-BADAS
Gemini 2.5 Pro Tuned
Qwen3-VL-2B
Cosmos Vanilla
BADAS 2.0
BADAS 1.0
BADAS 2.0 Flash
BADAS Open
Cosmos-BADAS
Gemini 2.5 Pro Tuned
Qwen3-VL-2B
Cosmos Vanilla

Reading this chart: For every positive clip, each model's prediction timeline is normalized so 0% = first prediction and 100% = labeled event. Predictions are binned into 10 equal intervals then averaged across clips. Curves are baseline-normalized per model so the y-axis shows each model's rise above its own floor. A steep ramp means confidence increases sharply as the event approaches; a flat line means the model outputs a near-constant score regardless of proximity to collision.

04 – See What the Model Sees

See What the Model Sees.
Know Why It Acts.

For the first time, a collision anticipation system explains itself. Attention heatmaps show where the model looks. BADAS-Reason tells you what to do and why. OEMs get integration confidence. Insurers get audit trails. Fleet operators get actionable alerts.

Explainability
Attention heatmaps reveal exactly what the model sees and focuses on during risk scenarios. Not a black box.
Attention heatmap – spatial focus shifts to the approaching vehicle
Actionability
Predicts the right action to take and explains why in natural language. "Brake immediately – a dark vehicle is crossing the intersection from the left directly into the ego vehicle's path."
BADAS-Reason – natural language action recommendation with reasoning
05 – From Cloud to Edge

Three Models. From Cloud to Edge.
GPU and CPU.

Cloud analytics teams run the full 300M model. Edge ADAS integrators deploy BADAS 2.0 Flash. IoT manufacturers ship BADAS 2.0 Flash Lite. Same architecture, same training, same world model – scaled to your needs.

BADAS 2.0
300M parameters
  • SOTA performance across all metrics
  • Best mTTA and early warning recall
  • Expert in rare long-tail cases
  • 34ms on A100 · 41ms on Jetson Thor
BADAS 2.0 Flash
86M parameters
  • End-device-optimized model
  • Expert in false alarm prevention
  • Outperforms BADAS 1.0 on every metric
  • 4.8ms on A100 · 12.5ms on Jetson Thor
BADAS 2.0 Flash Lite
22M parameters
  • Ultra-light model for IoT devices
  • Optimized for GPU and CPU inference
  • Rivals BADAS 1.0 at 14x fewer params
  • 2.8ms on A100 · 5.9ms on Jetson Thor
Performance vs Latency
Model
Params
AP
A100 (FP16)
Jetson Thor (TensorRT)
BADAS 2.0
300M
99.4%
34ms
41ms
BADAS 2.0 Flash
86M
99.0%
4.8ms
12.5ms
BADAS 2.0 Flash Lite
22M
98.4%
2.8ms
5.9ms
BADAS 2.0 Flash Lite loses only 1% AP while running 12x faster than the full model on A100 – and 7x faster on Thor. Deploy anywhere from cloud to edge device.
06 – The Science

V-JEPA2 Validates the World Model Thesis.

BADAS 2.0 is built on V-JEPA2 – the architecture Yann LeCun proposed as the foundation for world models and physical AI. Latent-space prediction optimizes for physical causality, not visual reconstruction. BADAS 2.0 is the proof that this thesis works for safety-critical applications.

350K Cameras
Real-world capture 100M+ miles/month
Nexar Atlas
GPS-validated pipeline 45 PB structured video
V-JEPA2
Self-supervised learning Latent-space prediction
BADAS 2.0
94.0% mAP · 4.6% FPR34ms · 91x smaller

BADAS 2.0 fine-tunes a V-JEPA2 ViT-L backbone (300M parameters, 24 transformer layers) end-to-end on 16-frame clips at 256×256 resolution and 8 fps. A future-prediction branch estimates the scene 1 second ahead and concatenates it with the current clip, giving the prediction head access to both present evidence and near-future dynamics. Domain-specific SSL pre-training on 2.25M unlabeled Nexar edge device clips is the critical enabler for the distilled edge variants.

Why V-JEPA2 matters: V-JEPA2 learns by predicting the latent-space representation of future video frames rather than reconstructing pixels. Pixel reconstruction optimizes for visual fidelity. Latent-space prediction optimizes for physical causality. For collision anticipation, you need a model that understands what will happen – not just what is happening.

~200,000 Labeled Videos. Zero Synthetic Data.

Most collision anticipation models are trained on synthetic data or small academic datasets. BADAS 2.0 is trained exclusively on real-world edge device footage from Nexar's network – the largest ego-centric driving dataset ever assembled for this task.

BADAS 2.0 is trained on ~200,000 labeled videos (~2M windowed clips) – a 5x expansion over v1.0. The corpus is assembled through intelligent data mining: BADAS 1.0 runs as an active oracle over millions of unlabeled Nexar drives, surfacing high-risk clips for human review.

The result: 99.4% AP at 4.6% FPR – a 58% reduction in false alarms over v1.0 on the sliding-window benchmark, with gains across all subgroups including the hardest long-tail categories.

Same Architecture. Better Data. Much Better Results.
1.5k
BADAS Open
~40Kc
BADAS 1.0
~200K
BADAS 2.0
Long Tail of Driving
Excels on rare, edge-case scenarios – animals, fog, snow, motorcyclists, infrastructure failures. 99.4% AP across all 10 long-tail categories where competitors collapse.
Physics, Not Pattern Matching
V-JEPA2 predicts latent-space representations of future frames. This optimizes for physical causality – what will happen – not visual similarity to training data.
World Model
Beyond Driving
Works on situations that have nothing to do with driving on the road directly – drones flying, vacuums cleaning, a forklift at work. This is because the model went beyond road rules. It learned physics.
BADAS 2.0 is not a driving model. It is a world model that happens to be deployed on roads.
Per-Category Dominance
BADAS 2.0 vs COSMOS across all 10 long-tail categories. BADAS leads in every single one.
BADAS 2.0
Cosmos-BADAS
COSMOS-Reason2
Models don't emerge from abstractions alone. They come from sustained exposure to reality.
– Yann LeCun, Turing Award Winner, Nexar Board Member
07 – Deployment

Where BADAS Is Used Today

AV Program Development

Ground truth for training, validating, and benchmarking autonomous systems. Access the world's largest library of outcome-verified edge cases.

ADAS Supplier Integration

API access to collision prediction as a feature layer. Ship BADAS as a premium safety tier on Snapdragon Ride or proprietary ADAS platforms.

Fleet Safety

Real-time collision risk scoring for commercial vehicle operations. Move from reactive incident management to predictive intervention.

Insurance Underwriting

BADAS risk scores as underwriting inputs. Behavior-based pricing backed by production-grade collision anticipation AI.
08 – FAQ

Frequently Asked Questions

What does BADAS stand for?
▼
BADAS stands for Beyond ADAS (Advanced Driver Assistance Systems). It is Nexar's collision anticipation system, now in its second generation. BADAS 2.0 fine-tunes V-JEPA2 on ~200,000 labeled edge device videos (~2M windowed clips) and achieves state-of-the-art accuracy across all public collision anticipation benchmarks.
Which benchmarks was BADAS tested on?
▼
BADAS 2.0 was evaluated on the Nexar Kaggle competition (1,344 clips, single window), a new 10-group long-tail benchmark (888 clips covering animal, pedestrian, cyclist, fog, rain, snow, intersection, infrastructure, passing/overtaking, and motorcyclist scenarios), and three public external benchmarks: DAD, DoTA, and DADA-2000 using ego-centric re-annotation and sliding-window evaluation.
What models was BADAS compared against?
▼
The paper compares five BADAS variants (2.0, 1.0, 2.0 Flash, 2.0 Flash Lite, Open) against four VLM baselines: Gemini-BADAS (Gemini 2.5 Pro fine-tuned on BADAS data), COSMOS-BADAS (NVIDIA COSMOS-Reason2-2B fine-tuned), vanilla Gemini 2.5 Pro, and Qwen3-VL-2B. Even after fine-tuning on the same data, autoregressive VLMs remain significantly below the BADAS family on the long-tail benchmark.
What architecture does BADAS 2.0 use?
▼
BADAS 2.0 fine-tunes V-JEPA2 (ViT-L, 300M parameters) end-to-end on edge device video. A future-prediction branch estimates the scene 1 second ahead, giving the classifier access to both present and anticipated dynamics. The distilled variants – BADAS 2.0 Flash at 86M (4x compression) and BADAS 2.0 Flash Lite at 22M (14x compression) – use domain-specific SSL pre-training followed by knowledge distillation to achieve near-parity accuracy at 7–12x faster inference.
How do I access BADAS?
▼
BADAS is available via a public API that lets you upload video, run predictions, and export results – including prediction overlays and attention heatmaps. Try the web-based playground at the top of this page for direct browser access. Enterprise partners can request full API access for integration into AV programs, fleets, or ADAS platforms.
09 – The Signal

Best Collision Prediction
Model on the Planet.

The Bar Is Set.

We publish our benchmarks. We built a public demo. If you think your model is better – show us. If you want to deploy the best – talk to us.

Try BADAS 2.0 →Learn more on BADAS 2.0
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