Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehicles

• URL: http://arxiv.org/abs/2605.04299v1
• Date: Tue, 05 May 2026 21:07:11 GMT
• Title: Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehicles
• Abstract summary: Scene understanding is a vital part of autonomous driving systems, which requires the use of deep learning models.<n>We introduce a confidence threshold sensitivity analysis that evaluates various threshold values to identify optimal decision boundaries for different tasks.<n>We introduce IUST-XAI-AD, a novel dataset consisting of 958 images with human annotations for driving decisions and corresponding reasoning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scene understanding is a vital part of autonomous driving systems, which requires the use of deep learning models. Deep learning methods are intrinsically black box models, which lack transparency and safety in autonomous driving. To make these systems transparent, multi-task visual understanding has become crucial for explainable autonomous driving perception systems, where simultaneous prediction of multiple driving behaviors and their underlying explanations is essential for safe navigation and human trust in autonomous vehicles. In order to design an accurate and cross-cultural explainable autonomous driving system, we introduce a comprehensive confidence threshold sensitivity analysis that evaluates various threshold values to identify optimal decision boundaries for different tasks. Our analysis demonstrates that traditional fixed threshold approaches are suboptimal for multi-task scenarios. Through extensive evaluation, we demonstrate that our adaptive threshold selection methodology improves F1-scores across different tasks. In addition, we introduce IUST-XAI-AD, a novel dataset consisting of 958 images with human annotations for driving decisions and corresponding reasoning. This dataset addresses the critical gap in domain-specific evaluation benchmarks for distinct driving contexts and provides a more challenging test environment compared to existing datasets. Experimental results demonstrate that confidence threshold sensitivity analysis can significantly improve model performance, while the introduction of the IUST-XAI-AD dataset reveals important insights about cross-cultural driving behavior patterns. The combined contributions of this work provide both methodological advances and practical evaluation tools that can accelerate the development of more reliable, explainable, and culturally-adaptive autonomous driving systems for global deployment.

Related papers

• KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System [44.93698347738791]
  We propose KnowVal, a new autonomous driving system that enables visual-language reasoning.<n>We construct a comprehensive driving knowledge graph that encodes traffic laws, defensive driving principles, and ethical norms.<n>KnowVal achieves the lowest collision rate on nuScenes and state-of-the-art results on Bench2Drive.
  arXiv  Detail & Related papers  (2025-12-23T12:08:00Z)
• DriveCritic: Towards Context-Aware, Human-Aligned Evaluation for Autonomous Driving with Vision-Language Models [24.168614747778538]
  We introduce DriveCritic, a novel framework featuring two key contributions.<n>The dataset is a curated collection of challenging scenarios where context is critical for correct judgment.<n>The DriveCritic model learns to adjudicate between trajectory pairs by integrating visual and symbolic context.
  arXiv  Detail & Related papers  (2025-10-15T03:00:38Z)
• Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving [55.13109926181247]
  We introduce ReflectDrive, a learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion.<n>Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient.<n>Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors.
  arXiv  Detail & Related papers  (2025-09-24T13:35:15Z)
• RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving [10.984203470464687]
  Vision-language models (VLMs) often suffer from limitations such as inadequate spatial perception and hallucination.<n>We propose a retrieval-augmented decision-making (RAD) framework to enhance VLMs' capabilities to reliably generate meta-actions in autonomous driving scenes.<n>We fine-tune VLMs on a dataset derived from the NuScenes dataset to enhance their spatial perception and bird's-eye view image comprehension capabilities.
  arXiv  Detail & Related papers  (2025-03-18T03:25:57Z)
• DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding [76.3876070043663]
  We propose DriveLMM-o1, a dataset and benchmark designed to advance step-wise visual reasoning for autonomous driving.<n>Our benchmark features over 18k VQA examples in the training set and more than 4k in the test set, covering diverse questions on perception, prediction, and planning.<n>Our model achieves a +7.49% gain in final answer accuracy, along with a 3.62% improvement in reasoning score over the previous best open-source model.
  arXiv  Detail & Related papers  (2025-03-13T17:59:01Z)
• SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models [63.71984266104757]
  We propose SafeAuto, a framework that enhances MLLM-based autonomous driving by incorporating both unstructured and structured knowledge.<n>To explicitly integrate safety knowledge, we develop a reasoning component that translates traffic rules into first-order logic.<n>Our Multimodal Retrieval-Augmented Generation model leverages video, control signals, and environmental attributes to learn from past driving experiences.
  arXiv  Detail & Related papers  (2025-02-28T21:53:47Z)
• Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
  We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
  arXiv  Detail & Related papers  (2024-07-09T04:56:11Z)
• Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving [38.28159034562901]
  Reason2Drive is a benchmark dataset with over 600K video-text pairs. We characterize the autonomous driving process as a sequential combination of perception, prediction, and reasoning steps. We introduce a novel aggregated evaluation metric to assess chain-based reasoning performance in autonomous systems.
  arXiv  Detail & Related papers  (2023-12-06T18:32:33Z)
• Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models [114.69732301904419]
  We present an approach to apply end-to-end open-set (any environment/scene) autonomous driving that is capable of providing driving decisions from representations queryable by image and text. Our approach demonstrates unparalleled results in diverse tests while achieving significantly greater robustness in out-of-distribution situations.
  arXiv  Detail & Related papers  (2023-10-26T17:56:35Z)
• Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving [100.3848723827869]
  We present an effective multi-task framework, VE-Prompt, which introduces visual exemplars via task-specific prompting. Specifically, we generate visual exemplars based on bounding boxes and color-based markers, which provide accurate visual appearances of target categories. We bridge transformer-based encoders and convolutional layers for efficient and accurate unified perception in autonomous driving.
  arXiv  Detail & Related papers  (2023-03-03T08:54:06Z)
• VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection [15.36267013724161]
  We propose a visual analytics system, VATLD, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications. The disentangled representation learning extracts data semantics to augment human cognition with human-friendly visual summarization. We also demonstrate the effectiveness of various performance improvement strategies with our visual analytics system, VATLD, and illustrate some practical implications for safety-critical applications in autonomous driving.
  arXiv  Detail & Related papers  (2020-09-27T22:39:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.