pith:SKLXIQYD
Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey
Compression strategies for Transformer autonomous driving models must be integrated into system design rather than applied afterward.
arxiv:2304.10891 v3 · 2023-04-21 · cs.LG · cs.AI · cs.CV · cs.RO · cs.SY · eess.SY
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Claims
Rather than treating compression as an isolated post-processing step, we highlight it as a system-level design consideration that directly affects deployability, robustness, and safety.
The survey assumes that the representative models and compression strategies selected from the literature are sufficiently complete and unbiased to support general statements about task-dependent applicability and design trade-offs.
A survey that organizes Transformer-based autonomous driving models by task and architecture while analyzing compression techniques as a system-level deployment concern.
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| First computed | 2026-06-04T01:08:24.249466Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
929774430366e32ebefd7af0fbba7aea80510d5165b7ed4779b707bdd16e1403
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· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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