Explore/dataset/CASCADE: A Spatio-Temporal-Causal Reasoning Representation and Dataset for Driving
C

Jenny Schmalfuss, Despoina Paschalidou, Simon Gerstenecker, German Ros, Jose M. Alvarez/CASCADE: A Spatio-Temporal-Causal Reasoning Representation and Dataset for DrivingUnknown

CASCADE is a human‑annotated dataset and structured scene representation for spatio‑temporal causal reasoning in autonomous driving. It provides 2,066 driving clips with detailed action, location, and causal link annotations to enable verifiable reasoning evaluation.

dataset
GitHubCompare
Refreshed 6h ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads—30d
Last commit—
Refreshed6h ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
55 / 100
Composite score from 6 signals. How we score →
Active project
55Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 0 days observed0 not enough history
snapshots
not enough history
Repository activity · 0 days observednot enough history from pushed_at
inactivepushed
not enough history
not enough history
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language—
License—
Sourcearxiv
Open source✗ No
Commercial use—
Docs—
Demo—

AgentHub Score

55
Score 55/100
Below average

Alternatives

D
Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?
0 · dataset
55Compare all →

Recent activity

Latest commit ——
Indexed by AgentHub crawler6h ago
Monitor for new releasesongoing