[Paper] Coverage-Guided Road Selection and Prioritization for Efficient Testing in Autonomous Driving Systems

Source: arXiv - 2601.08609v1

Overview

Testing autonomous driving assistance systems (ADAS) is a massive undertaking—thousands of road scenarios must be run to catch safety‑critical bugs. However, many of these scenarios are near‑duplicates, inflating test time without adding value. The paper “Coverage‑Guided Road Selection and Prioritization for Efficient Testing in Autonomous Driving Systems” proposes a data‑driven framework that trims redundant roads, guarantees diverse coverage, and orders the remaining tests so the most challenging, failure‑prone cases run first.

Key Contributions

• Redundancy‑aware clustering of road scenarios using both geometric (e.g., curvature, lane layout) and dynamic ADAS behavior features (e.g., steering, speed profiles).
• Representative selection from each cluster that preserves geometric and behavioral diversity while dramatically shrinking the test suite.
• Multi‑factor prioritization that ranks selected roads by geometric complexity, driving difficulty, and historic failure frequency.
• Empirical validation on the OPENCAT dataset and the Udacity self‑driving simulator, showing up to 89 % reduction in test size and 95× faster early failure detection versus random ordering.

Methodology

1. Feature Extraction – For every road scenario, the authors compute a vector of geometric descriptors (lane curvature, intersection count, elevation changes) and dynamic descriptors derived from the ADAS’s own trajectory (steering angle variance, speed fluctuations).
2. Clustering – Using a density‑based algorithm (e.g., DBSCAN), scenarios that are close in this combined feature space are grouped together. Each cluster represents a “type” of road‑driving interaction.
3. Representative Picking – Within each cluster, the scenario that is most central (minimum average distance to other members) is chosen as the cluster’s representative. This guarantees that the reduced suite still covers the full spectrum of road‑behavior patterns.
4. Prioritization Scoring – A weighted score is computed for each representative:
   • Geometric complexity (sharp turns, many lane changes)
   • Driving difficulty (high variance in speed/steering)
   • Historical failure rate (how often the ADAS previously crashed on similar roads)
     The scores are sorted descending, yielding the final execution order.
5. Evaluation – The pipeline is applied to two ADAS implementations (a lane‑keeping controller and a combined lane‑keeping + adaptive cruise control) and the results are compared against a random baseline and a naïve “first‑come‑first‑served” ordering.

Results & Findings

In plain terms, the approach cuts the number of runs needed by almost an order of magnitude while still catching the bulk of bugs, and it surfaces the hardest‑to‑pass cases almost immediately.

Practical Implications

• Faster CI pipelines – Teams can integrate the clustering‑selection step into their continuous‑integration (CI) workflow, slashing regression test times from hours to minutes.
• Resource‑efficient simulation – Cloud‑based simulation farms can allocate fewer GPU/CPU hours per build, reducing cost.
• Targeted safety analysis – By surfacing high‑complexity, high‑failure roads early, engineers can focus debugging effort where it matters most, accelerating root‑cause analysis.
• Dataset curation – The clustering logic can be repurposed to clean and balance public road‑scenario datasets, making them more useful for benchmarking new ADAS models.

Limitations & Future Work

• Feature dependence – The clustering quality hinges on the chosen geometric and dynamic descriptors; exotic road features (e.g., weather effects) are not yet captured.
• Static weighting – Prioritization weights are manually set; adaptive learning of these weights from live failure logs could improve robustness.
• Scalability to massive fleets – While effective on OPENCAT (~10k scenarios), the authors note that ultra‑large corpora (millions of scenarios) may require hierarchical clustering or streaming algorithms.
• Generalization across ADAS types – The study focuses on lane‑keeping and adaptive cruise control; extending to perception‑heavy modules (e.g., object detection) remains an open question.

Authors

• Qurban Ali
• Andrea Stocco
• Leonardo Mariani
• Oliviero Riganelli

Paper Information

• arXiv ID: 2601.08609v1
• Categories: cs.SE
• Published: January 13, 2026
• PDF: Download PDF