arXiv subjects

Michiel Braat

Publications and source records attributed to Michiel Braat.

4 recordsLinked to original sources

arXiv2026

A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving

Software-in-the-loop (SIL) simulation is a cornerstone for the validation of modern automotive safety functions. However, many current frameworks utilize ideal sensing, which bypasses the functional insufficiencies of perception algorithms, leading to over-optimistic safety assessments. This paper proposes a perception-informed SIL testing methodology that bridges the gap between ground-truth simulation and real-world perception behavior. We present a framework for incorporating causal probabilistic models into standardized, scenario-based simulation toolchains, applicable to both Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). Our approach enables the systematic injection of realistic perception errors, such as loss of detection, sizing inaccuracies, and positioning offsets, derived from physical triggering conditions like fog, rain, and object-merging scenarios. By evaluating these ``faults'' within a standardized simulation environment, we demonstrate that perception-informed testing reveals latent operational risks that ideal SIL environments fail to capture, providing a scalable pathway for SOTIF (ISO 21448) validation.

cs.RO↗

arXiv2025

What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Planners

Automated driving functions increasingly rely on machine learning for tasks like perception and trajectory planning, requiring large, relevant datasets. The performance of these algorithms depends on how closely the training data matches the task. To ensure reliable functioning, it is crucial to know what is included in the dataset to assess the trained model's operational risk. We aim to enhance the safe use of machine learning in automated driving by developing a method to recognize situations that an automated vehicle has not been sufficiently trained on. This method also improves explainability by describing the dataset at a human-understandable level. We propose modeling driving data as knowledge graphs, representing driving scenes with entities and their relationships. These graphs are queried for specific sub-scene configurations to check their occurrence in the dataset. We estimate a vehicle's competence in a driving scene by considering the coverage and complexity of sub-scene configurations in the training set. Higher complexity scenes require greater coverage for high competence. We apply this method to the NuPlan dataset, modeling it with knowledge graphs and analyzing the coverage of specific driving scenes. This approach helps monitor the competence of machine learning models trained on the dataset, which is essential for trustworthy AI to be deployed in automated driving.

cs.RO↗

arXiv2024

LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration

Predicting the behavior of road users accurately is crucial to enable the safe operation of autonomous vehicles in urban or densely populated areas. Therefore, there has been a growing interest in time series motion prediction research, leading to significant advancements in state-of-the-art techniques in recent years. However, the potential of using LiDAR data to capture more detailed local features, such as a person's gaze or posture, remains largely unexplored. To address this, we develop a novel multimodal approach for motion prediction based on the PointNet foundation model architecture, incorporating local LiDAR features. Evaluation on the Waymo Open Dataset shows a performance improvement of 6.20% and 1.58% in minADE and mAP respectively, when integrated and compared with the previous state-of-the-art MTR. We open-source the code of our LiMTR model.

cs.LG↗

arXiv2023

Connecting the Dots: Context-Driven Motion Planning Using Symbolic Reasoning

The introduction of highly automated vehicles on the public road may improve safety and comfort, although its success will depend on social acceptance. This requires trajectory planning methods that provide safe, proactive, and comfortable trajectories that are risk-averse, take into account predictions of other road users, and comply with traffic rules, social norms, and contextual information. To consider these criteria, in this article, we propose a non-linear model-predictive trajectory generator. The problem space is populated with risk fields. These fields are constructed using a novel application of a knowledge graph, which uses a traffic-oriented ontology to reason about the risk of objects and infrastructural elements, depending on their position, relative velocity, and classification, as well as depending on the implicit context, driven by, e.g., social norms or traffic rules. Through this novel combination, an adaptive trajectory generator is formulated which is validated in simulation through 4 use cases and 309 variations and is shown to comply with the relevant social norms, while taking minimal risk and progressing towards a goal area.

math.OC↗