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ARXIV:2603.26462 · AUTONOMOUS VEHICLE SAFETY · SUBMITTED 30 MAR · 22:20 UTC · FRESHNESS STALE
ARXIV:2603.26462AUTONOMOUS VEHICLE SAFETYSUBMITTED 30 MAR · 22:20 UTCFRESHNESS STALEJiaxiang Li · Jun Yan · Daniel Watzenig · Huilin Yin · arXiv
A black-box adversarial attack framework for trajectory prediction systems that exploits vulnerabilities in autonomous vehicle safety.
Opportunity summary
Pain A black-box adversarial attack framework for trajectory prediction systems that exploits vulnerabilities in autonomous vehicle safety.
Evidence 19 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A black-box adversarial attack framework for trajectory prediction systems that exploits vulnerabilities in autonomous vehicle safety. Existing attack methods require white-box access with gradient information and rely on rigid physical constraints, limiting real-world applicability.
Trajectory prediction systems are critical for autonomous vehicle safety, yet remain vulnerable to adversarial attacks that can cause catastrophic traffic behavior misinterpretations. Existing attack methods require white-box access with gradient information and rely on…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Unlike existing approaches, our method supports both intention misclassification attacks and prediction accuracy degradation. Code availability is flagged in the production record; the public…
Autonomous Vehicle Safety moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A black-box adversarial attack framework for trajectory prediction systems that exploits vulnerabilities in autonomous vehicle safety.
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Paper Pack
10.48550/arXiv.2603.26462A black-box adversarial attack framework for trajectory prediction systems that exploits vulnerabilities in autonomous vehicle safety.
Abstract
Trajectory prediction systems are critical for autonomous vehicle safety, yet remain vulnerable to adversarial attacks that can cause catastrophic traffic behavior misinterpretations. Existing attack methods require white-box access with gradient information and rely on rigid physical constraints, limiting real-world applicability. We propose DTP-Attack, a decision-based black-box adversarial attack framework tailored for trajectory prediction systems. Our method operates exclusively on binary decision outputs without requiring model internals or gradients, making it practical for real-world scenarios. DTP-Attack employs a novel boundary walking algorithm that navigates adversarial regions without fixed constraints, naturally maintaining trajectory realism through proximity preservation. Unlike existing approaches, our method supports both intention misclassification attacks and prediction accuracy degradation. Extensive evaluation on nuScenes and Apolloscape datasets across state-of-the-art models including Trajectron++ and Grip++ demonstrates superior performance. DTP-Attack achieves 41 - 81% attack success rates for intention misclassification attacks that manipulate perceived driving maneuvers with perturbations below 0.45 m, and increases prediction errors by 1.9 - 4.2 for accuracy degradation. Our method consistently outperforms existing black-box approaches while maintaining high controllability and reliability across diverse scenarios. These results reveal fundamental vulnerabilities in current trajectory prediction systems, highlighting urgent needs for robust defenses in safety-critical autonomous driving applications.
Source availability
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Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
unverified19 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
A black-box adversarial attack framework for trajectory prediction systems that exploits vulnerabilities in autonomous vehicle safety. Existing attack methods require white-box access with gradient information and rely on rigid physical constraints, limiting real-world applicabi...
METHOD
Trajectory prediction systems are critical for autonomous vehicle safety, yet remain vulnerable to adversarial attacks that can cause catastrophic traffic behavior misinterpretations. Existing attack methods require white-box access with gradient information and rely on rigid ph...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Unlike existing approaches, our method supports both intention misclassification attacks and prediction accuracy degradation. Code availability is flagged in the production record; the public repository l...
WHY NOW
Autonomous Vehicle Safety moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
We propose DTP-Attack, a decision-based black-box adversarial attack framework tailored for trajectory prediction systems.
This is the core assertion of the paper, stated multiple times in the abstract and conclusion.
partial
Our method operates exclusively on binary decision outputs without requiring model internals or gradients, making it practical for real-world scenarios.
This is a key differentiator of the proposed method and is explicitly stated in the abstract and conclusion.
partial
DTP-Attack employs a novel boundary walking algorithm that navigates adversarial regions without fixed constraints, naturally maintaining trajectory realism through proximity preservation.
The abstract and conclusion highlight the novel algorithm used by DTP-Attack.
partial
DTP-Attack achieves 41 - 81% attack success rates for intention misclassification attacks that manipulate perceived driving maneuvers with perturbations below 0.45 m
Specific numerical results for attack success rate and perturbation magnitude are provided in the abstract and a results table.
partial
and increases prediction errors by 1.9 - 4.2 for accuracy degradation.
Specific numerical results for prediction error increase are provided in the abstract and a results table.
partial
Existing attack methods require white-box access with gradient information and rely on rigid physical constraints, limiting real-world applicability.
This is presented as a limitation of prior work, motivating the development of DTP-Attack.
partial
Unlike existing approaches, our method supports both intention misclassification attacks and prediction accuracy degradation.
The abstract and a section in the paper explicitly state the two types of attacks supported by DTP-Attack.
partial
We propose DTP-Attack, a decision-based black-box adversarial attack framework tailored for trajectory prediction systems.
This is the core claim of the paper, stated multiple times in the abstract and conclusion.
partial
Our method operates exclusively on binary decision outputs without requiring model internals or gradients, making it practical for real-world scenarios.
This is a key differentiator of the proposed method, explicitly stated in the abstract and conclusion.
partial
DTP-Attack employs a novel boundary walking algorithm that navigates adversarial regions without fixed constraints, naturally maintaining trajectory realism through proximity preservation.
The abstract and conclusion highlight the novel algorithm used by DTP-Attack.
partial
DTP-Attack achieves 41 - 81% attack success rates for intention misclassification attacks that manipulate perceived driving maneuvers with perturbations below 0.45 m
Specific numerical results for attack success rate and perturbation magnitude are provided in the abstract and a results table.
partial
and increases prediction errors by 1.9 - 4.2 for accuracy degradation.
Specific numerical results for prediction error increase are provided in the abstract and a results table.
partial
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Concepts
Methods
Materials
Markets
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A black-box adversarial attack framework for trajectory prediction systems that exploits vulnerabilities in autonomous vehicle safety.
Segment
Autonomous Vehicle Safety
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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3/3 checks · 100%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
19 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
19 references, 3 sources, 50% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
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Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
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Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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