Pith. sign in

REVIEW 3 cited by

AmbieGen: A Search-based Framework for Autonomous Systems Testing

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2301.01234 v1 pith:ZXVIKKHD submitted 2023-01-01 cs.RO cs.NE

Dmytro Humeniuk , Foutse Khomh , Giuliano Antoniol This is my paper · ORCID
classification cs.ROcs.NE
keywords autonomoussystemsambiegentesttestingframeworkscenariossearch
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Thorough testing of safety-critical autonomous systems, such as self-driving cars, autonomous robots, and drones, is essential for detecting potential failures before deployment. One crucial testing stage is model-in-the-loop testing, where the system model is evaluated by executing various scenarios in a simulator. However, the search space of possible parameters defining these test scenarios is vast, and simulating all combinations is computationally infeasible. To address this challenge, we introduce AmbieGen, a search-based test case generation framework for autonomous systems. AmbieGen uses evolutionary search to identify the most critical scenarios for a given system, and has a modular architecture that allows for the addition of new systems under test, algorithms, and search operators. Currently, AmbieGen supports test case generation for autonomous robots and autonomous car lane keeping assist systems. In this paper, we provide a high-level overview of the framework's architecture and demonstrate its practical use cases.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs

    cs.SD 2026-02 conditional novelty 6.0 of 10

    Adding classical shape-gain decomposition to a neural audio codec makes it invariant to input gain and improves bitrate-distortion performance.

  2. DURA-CPS: A Multi-Role Orchestrator for Dependability Assurance in LLM-Enabled Cyber-Physical Systems

    cs.RO 2025-06 conditional novelty 6.0 of 10

    DURA-CPS is a multi-role orchestration framework that iteratively assesses safety, security, performance, and recovery of AI components in simulated cyber-physical systems.

  3. Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost

    cs.LG 2025-07 reject novelty 3.0 of 10

    On 55 million NYC taxi trips, XGBoost outperforms GAT and TimesNet on clean and noisy fare prediction, but the comparison is weakened by missing error bars and contradictory claims.

Pith tools