ARXIV:2602.23259 · AUTONOMOUS VEHICLE CONTROL SYSTEMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

Q: What products could be built from this research?

To productize this technology, creating a middleware or API can integrate into existing autonomous vehicle systems, providing enhanced risk assessment and decision-making capabilities.

Q: What are the practical use cases?

A commercial application could be an autonomous driving software that integrates into existing automotive platforms, offering improved safety features that do not rely directly on expert driving data, suitable for vehicle manufacturers.

Q: What industries could this research disrupt?

It has the potential to replace or supplement current expert-based autonomous driving models easily affected by unanticipated driving scenarios, leading to greater reliability in diverse environments.

arXiv

RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations. Currently, IL-based methods have become a mainstream paradigm: models rely on standard driving behaviors given by…

METHOD

Full abstract

With advances in imitation learning (IL) and large-scale driving datasets, end-to-end autonomous driving (E2E-AD) has made great progress recently. Currently, IL-based methods have become a mainstream paradigm: models rely on standard driving behaviors given by experts, and learn to minimize the discrepancy between their actions and expert actions. However, this objective of "only driving like the expert" suffers from limited generalization: when encountering rare or unseen long-tail scenarios outside the distribution of expert demonstrations, models tend to produce unsafe decisions in the absence of prior experience. This raises a fundamental question: Can an E2E-AD system make reliable decisions without any expert action supervision? Motivated by this, we propose a unified framework named Risk-aware World Model Predictive Control (RaWMPC) to address this generalization dilemma through robust control, without reliance on expert demonstrations. Practically, RaWMPC leverages a world model to predict the consequences of multiple candidate actions and selects low-risk actions through explicit risk evaluation. To endow the world model with the ability to predict the outcomes of risky driving behaviors, we design a risk-aware interaction strategy that systematically exposes the world model to hazardous behaviors, making catastrophic outcomes predictable and thus avoidable. Furthermore, to generate low-risk candidate actions at test time, we introduce a self-evaluation distillation method to distill riskavoidance capabilities from the well-trained world model into a generative action proposal network without any expert demonstration. Extensive experiments show that RaWMPC outperforms state-of-the-art methods in both in-distribution and out-of-distribution scenarios, while providing superior decision interpretability.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. Extensive experiments show that RaWMPC outperforms state-of-the-art methods in both in-distribution and out-of-distribution scenarios, while providing superior decision interpretability.

WHY NOW

Autonomous Vehicle Control Systems moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainRaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.23259 · AUTONOMOUS VEHICLE CONTROL SYSTEMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

arXiv

RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Autonomous Vehicle Control Systems moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainRaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.23259

Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback97 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

6.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Autonomous Vehicle Control Systems moved forward this cycle; last verified April 2026. Public score 6.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations. Currently, IL-based methods have become a mainstream paradigm: models rely on standard driving behaviors given by experts, and learn to minimize the discrepancy between their actions and expert actions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
With advances in imitation learning (IL) and large-scale driving datasets, end-to-end autonomous driving (E2E-AD) has made great progress recently. Currently, IL-based methods have become a mainstream paradigm: models rely on standard driving behaviors given by experts, and learn to minimize the discrepancy between their actions and expert actions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Extensive experiments show that RaWMPC outperforms state-of-the-art methods in both in-distribution and out-of-distribution scenarios, while providing superior decision interpretability.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Autonomous Vehicle Control Systems moved forward this cycle; last verified April 2026. Public score 6.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Autonomous Vehicle Control Systems

Competitors

not indexed

Competitive landscape

RaWMPC uses risk-aware predictive control to enhance the safety and reliability of autonomous driving systems without expert demonstrations.

Segment

Autonomous Vehicle Control Systems

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.23259 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(97)

DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving

2025Shuyao Shang, Yuntao Chen et al.

DINOv3

2025Oriane Sim'eoni, Huy V. Vo et al.

ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving

2025Yuhang Lu, Jiadong Tu et al.

World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model

2025Yupeng Zheng, Pengxuan Yang et al.

Epona: Autoregressive Diffusion World Model for Autonomous Driving

2025Kaiwen Zhang, Zhenyu Tang et al.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models

2025Shadi Hamdan, Chonghao Sima et al.

Pseudo-Simulation for Autonomous Driving

2025Wei Cao, Marcel Hallgarten et al.

End-to-End Driving with Online Trajectory Evaluation via BEV World Model

2025Yingyan Li, Yu-Quan Wang et al.

ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation

2025Haoyu Fu, Diankun Zhang et al.

Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning

2025Bozhou Zhang, Nan Song et al.

Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training

2025Zhenxin Li, Shihao Wang et al.

SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment

2025Katrin Renz, Long Chen et al.

HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder

2025Yingqi Tang, Zhuo Xu et al.

GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving

2025Zebin Xing, Xingyu Zhang et al.

DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving

2025Xiaosong Jia, Junqi You et al.

Don’t Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving

2025Ziying Song, Caiyan Jia et al.

MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction

2025J. Ni, Yuxin Guo et al.

M^3PC: Test-time Model Predictive Control using Pretrained Masked Trajectory Model

2025Kehan Wen, Yutong Hu et al.

DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers

2024Yuntao Chen, Yu-Quan Wang et al.

GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction

2024Sicheng Zuo, Wenzhao Zheng et al.

Showing 20 of 97 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisBeyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning

5.0

Builds On ThisUncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving

4.0

Commercially relevant

Higher ViabilityCausality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling

7.0

Higher ViabilityDriver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout

7.0

Higher ViabilityPerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning

8.0

Higher ViabilityBehavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control

7.0

Higher ViabilityDreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving

7.0

Higher ViabilityLMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving

8.0

Higher ViabilityTowards Driver Behavior Understanding: Weakly-Supervised Risk Perception in Driving Scenes

8.0

Higher ViabilityNeuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control

7.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.23259
Route: /paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving
Active tab: read
Artifact: risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving/paper-pack
REST build passport API/api/v1/paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "arxiv_id": "2602.23259",
  "canonical_route": "/paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving",
  "endpoints": {
    "paper_pack": "/api/v1/paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving/paper-pack",
    "build_passport": "/api/v1/paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

Canonical ID risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving | Route /paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.23259"
  }
}

source_context

{
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  "mode": "paper",
  "query": "Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving",
  "normalized_query": "2602.23259",
  "route": "/paper/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving",
  "paper_ref": "risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

/buildability/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving

Watchwatch

Subject: Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving

Paper ref

risk-aware-world-model-predictive-control-for-generalizable-end-to-end-autonomous-driving

arXiv id

2602.23259

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

302269e67c2e73ac99d53d4585f0e0a8340688aee21ab38d3a2c10081bff01c2

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

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