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

arXiv: https://arxiv.org/abs/2605.04299 · HTML: https://mlsanity.com/paper/2605.04299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 5 May 2026.

## Builds on (most cited first, 16 of 16)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018)
- [Explainable Object-induced Action Decision for Autonomous Vehicles](https://mlsanity.com/paper/2003.09405/) (2020)
- [EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference](https://mlsanity.com/paper/2011.14203/) (2020)
- [Deep Object-Centric Policies for Autonomous Driving](https://mlsanity.com/paper/1811.05432/) (2018)
- [Revisiting RCNN: On Awakening the Classification Power of Faster RCNN](https://mlsanity.com/paper/1803.06799/) (2018)
- [Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios](https://mlsanity.com/paper/2005.02031/) (2020)
- [Confidence estimation of classification based on the distribution of the neural network output layer](https://mlsanity.com/paper/2210.07745/) (2022)
- [A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning](https://mlsanity.com/paper/2206.08783/) (2022)
- [Incremental Deep Neural Network Learning using Classification Confidence Thresholding](https://mlsanity.com/paper/2106.11437/) (2021)
- [Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking](https://mlsanity.com/paper/2312.01650/) (2023)
- [A Configurable Simulation Framework for Safety Assessment of Vulnerable Road Users](https://mlsanity.com/paper/2510.19097/) (2025)
- [An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving](https://mlsanity.com/paper/2605.00291/) (2026)

## Abstract (arXiv)

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.