From Human Intention to Action Prediction: A Comprehensive Benchmark for Intention-driven End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2512.12302v1
• Date: Sat, 13 Dec 2025 11:59:51 GMT
• Title: From Human Intention to Action Prediction: A Comprehensive Benchmark for Intention-driven End-to-End Autonomous Driving
• Authors: Huan Zheng, Yucheng Zhou, Tianyi Yan, Jiayi Su, Hongjun Chen, Dubing Chen, Wencheng Han, Runzhou Tao, Zhongying Qiu, Jianfei Yang, Jianbing Shen,
• Abstract summary: Current autonomous driving systems operate at a level of intelligence akin to following simple steering commands.<n>We introduce Intention-Drive, the first comprehensive benchmark designed to evaluate the ability to translate high-level human intent into safe and precise driving actions.
• Score: 67.23302649816466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current end-to-end autonomous driving systems operate at a level of intelligence akin to following simple steering commands. However, achieving genuinely intelligent autonomy requires a paradigm shift: moving from merely executing low-level instructions to understanding and fulfilling high-level, abstract human intentions. This leap from a command-follower to an intention-fulfiller, as illustrated in our conceptual framework, is hindered by a fundamental challenge: the absence of a standardized benchmark to measure and drive progress on this complex task. To address this critical gap, we introduce Intention-Drive, the first comprehensive benchmark designed to evaluate the ability to translate high-level human intent into safe and precise driving actions. Intention-Drive features two core contributions: (1) a new dataset of complex scenarios paired with corresponding natural language intentions, and (2) a novel evaluation protocol centered on the Intent Success Rate (ISR), which assesses the semantic fulfillment of the human's goal beyond simple geometric accuracy. Through an extensive evaluation of a spectrum of baseline models on Intention-Drive, we reveal a significant performance deficit, showing that the baseline model struggle to achieve the comprehensive scene and intention understanding required for this advanced task.

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