Selected work

Representative Papers

Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal

Jul 13, 2026

In safety-critical and regulated domains, existing requirements engineering practices struggle to systematically support explainability requirements, facing challenges such as conceptual ambiguity, insufficient expressiveness in specification, and fragmented validation approaches. This study employs a multi-stage qualitative methodology—including think-aloud protocols, facilitated group discussions, and cross-phase analysis of requirements engineering activities—to investigate how Daimler Truck engineers address explainability requirements in real-world projects. For the first time, it identifies explainability challenges that span the entire requirements lifecycle—from elicitation and specification to verification—thereby laying an empirical foundation for an explainable AI (XAI)-oriented requirements engineering framework and addressing the notable gap in practice-driven approaches within this field.

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Real-Time Rulebook-Aware Nonlinear MPC for Autonomous Driving with Priority-Biased Tiered Slacks

Jul 12, 2026

This work addresses the challenge of autonomous driving motion planning under real-time constraints, requiring a balance among safety, rule compliance, comfort, and efficiency while enabling auditable decision-making. The authors propose W-SQP, a nonlinear model predictive controller based on weighted hierarchical slack variables, which encodes nine categories of driving rules into a four-layer nonlinear program with shared slack variables. A strongly separated hierarchical penalty mechanism prioritizes satisfaction of higher-priority rules while preserving hard actuator constraints. Leveraging CasADi and IPOPT, the system solves the optimization problem online at 10 Hz, guaranteeing feasible solutions at every time step and logging rule residuals for auditability. In closed-loop evaluations across 150 scenarios from the Waymo Open Motion Dataset, the method exhibits no systematic failures in safety or compliance metrics, with only localized performance degradation observed in highly ambiguous, complex scenes.

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RECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selection

May 24, 2026

This work addresses the limitations of existing autonomous driving systems that rely solely on model confidence for trajectory selection, often neglecting multi-level constraints such as safety, traffic regulations, and comfort, thereby leading to frequent violations. To remedy this, the authors propose a hierarchical rulebook-based trajectory reranking method grounded in a priority ordering: safety ≻ legality ≻ road rules ≻ comfort. Their approach integrates differentiable rule proxies and a scene-conditioned applicability mechanism, coupled with a deterministic ε-lexicographic decision policy, enabling provable rule compliance without retraining the underlying prediction model. Evaluated on the Waymo Open Motion Dataset, the method reduces safety and legal violation rates from 28.58% to 20.42% and overall violations from 40.32% to 32.41% compared to confidence-based selection, while maintaining 96% robust rejection capability under adversarial confidence perturbations.

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FugSeg: Fast Uncertainty-aware Ground Segmentation for 3D Point Cloud

May 09, 2026

This work addresses the challenge of ground segmentation in LiDAR point clouds caused by reflective noise and isolated ground points. The authors propose a polar grid map–based method that employs intra-segment and inter-segment ground labeling strategies to identify visible, occluded, and isolated ground cells. Reflective noise is explicitly modeled through the introduction of “noisy ground cells,” while an uncertainty-aware adaptive slope mechanism enhances robustness in complex terrains. Point-level segmentation is achieved via fine-grained elevation estimation. Evaluated on four public datasets, the method outperforms existing non-learning approaches, achieving state-of-the-art F1 score, accuracy, and mean Intersection over Union (mIoU). It runs at 135 Hz for 64-beam and 487 Hz for 32-beam LiDAR scans on a single CPU thread.

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Event-Chain Analysis for Automated Driving and ADAS Systems: Ensuring Safety and Meeting Regulatory Timing Requirements

Nov 22, 2025

Autonomous Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS) must comply with stringent end-to-end timing constraints mandated by international standards such as ISO 26262 and UN-R155; however, existing approaches lack transparent, white-box modeling and verifiable analysis of timing behavior across the full perception–planning–control–human–machine interaction pipeline. This paper proposes an event-chain-based white-box timing modeling and analysis framework that, for the first time, tightly couples functional safety requirements with regulatory timing constraints. The method enables architecture-level end-to-end timing constraint derivation, probabilistic timing verification, and timing-aware parameter optimization. Through model-based simulation and case studies, it facilitates early detection of timing violations, significantly improves design efficiency, and generates auditable, quantitative evidence of compliance—establishing a novel paradigm for standards conformance certification.

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Recent publications

Latest Papers

Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal

Jul 13, 2026

In safety-critical and regulated domains, existing requirements engineering practices struggle to systematically support explainability requirements, facing challenges such as conceptual ambiguity, insufficient expressiveness in specification, and fragmented validation approaches. This study employs a multi-stage qualitative methodology—including think-aloud protocols, facilitated group discussions, and cross-phase analysis of requirements engineering activities—to investigate how Daimler Truck engineers address explainability requirements in real-world projects. For the first time, it identifies explainability challenges that span the entire requirements lifecycle—from elicitation and specification to verification—thereby laying an empirical foundation for an explainable AI (XAI)-oriented requirements engineering framework and addressing the notable gap in practice-driven approaches within this field.

0 citationsRead paper

Real-Time Rulebook-Aware Nonlinear MPC for Autonomous Driving with Priority-Biased Tiered Slacks

Jul 12, 2026

This work addresses the challenge of autonomous driving motion planning under real-time constraints, requiring a balance among safety, rule compliance, comfort, and efficiency while enabling auditable decision-making. The authors propose W-SQP, a nonlinear model predictive controller based on weighted hierarchical slack variables, which encodes nine categories of driving rules into a four-layer nonlinear program with shared slack variables. A strongly separated hierarchical penalty mechanism prioritizes satisfaction of higher-priority rules while preserving hard actuator constraints. Leveraging CasADi and IPOPT, the system solves the optimization problem online at 10 Hz, guaranteeing feasible solutions at every time step and logging rule residuals for auditability. In closed-loop evaluations across 150 scenarios from the Waymo Open Motion Dataset, the method exhibits no systematic failures in safety or compliance metrics, with only localized performance degradation observed in highly ambiguous, complex scenes.

0 citationsRead paper

RECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selection

May 24, 2026

This work addresses the limitations of existing autonomous driving systems that rely solely on model confidence for trajectory selection, often neglecting multi-level constraints such as safety, traffic regulations, and comfort, thereby leading to frequent violations. To remedy this, the authors propose a hierarchical rulebook-based trajectory reranking method grounded in a priority ordering: safety ≻ legality ≻ road rules ≻ comfort. Their approach integrates differentiable rule proxies and a scene-conditioned applicability mechanism, coupled with a deterministic ε-lexicographic decision policy, enabling provable rule compliance without retraining the underlying prediction model. Evaluated on the Waymo Open Motion Dataset, the method reduces safety and legal violation rates from 28.58% to 20.42% and overall violations from 40.32% to 32.41% compared to confidence-based selection, while maintaining 96% robust rejection capability under adversarial confidence perturbations.

0 citationsRead paper

FugSeg: Fast Uncertainty-aware Ground Segmentation for 3D Point Cloud

May 09, 2026

This work addresses the challenge of ground segmentation in LiDAR point clouds caused by reflective noise and isolated ground points. The authors propose a polar grid map–based method that employs intra-segment and inter-segment ground labeling strategies to identify visible, occluded, and isolated ground cells. Reflective noise is explicitly modeled through the introduction of “noisy ground cells,” while an uncertainty-aware adaptive slope mechanism enhances robustness in complex terrains. Point-level segmentation is achieved via fine-grained elevation estimation. Evaluated on four public datasets, the method outperforms existing non-learning approaches, achieving state-of-the-art F1 score, accuracy, and mean Intersection over Union (mIoU). It runs at 135 Hz for 64-beam and 487 Hz for 32-beam LiDAR scans on a single CPU thread.

0 citationsRead paper

Event-Chain Analysis for Automated Driving and ADAS Systems: Ensuring Safety and Meeting Regulatory Timing Requirements

Nov 22, 2025

Autonomous Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS) must comply with stringent end-to-end timing constraints mandated by international standards such as ISO 26262 and UN-R155; however, existing approaches lack transparent, white-box modeling and verifiable analysis of timing behavior across the full perception–planning–control–human–machine interaction pipeline. This paper proposes an event-chain-based white-box timing modeling and analysis framework that, for the first time, tightly couples functional safety requirements with regulatory timing constraints. The method enables architecture-level end-to-end timing constraint derivation, probabilistic timing verification, and timing-aware parameter optimization. Through model-based simulation and case studies, it facilitates early detection of timing violations, significantly improves design efficiency, and generates auditable, quantitative evidence of compliance—establishing a novel paradigm for standards conformance certification.

0 citationsRead paper