随着自动驾驶技术的快速发展,如何保证自动驾驶系统的安全性变得愈发重要,因此预期功能安全(Safety of The Intended Functionality, SOTIF)的概念应运而生,它主要是为了减少由于系统非预期的感知和决策错误而引起的危害。本文提出了一...随着自动驾驶技术的快速发展,如何保证自动驾驶系统的安全性变得愈发重要,因此预期功能安全(Safety of The Intended Functionality, SOTIF)的概念应运而生,它主要是为了减少由于系统非预期的感知和决策错误而引起的危害。本文提出了一种依托自然驾驶数据的SOTIF自动化生成测试用例的方法。通过采集360°IBEO与环视摄像头数据,分析了4000多个前车切入场景,对关键变量进行参数化建模。采用改进的Monte-Carlo抽样技术,处理独立与非独立随机变量的联合分布,生成覆盖广泛场景的测试用例。实验结果表明该方法显著提升了测试用例生成效率,全面覆盖边角、危险及极端场景,解决了SOTIF测试中自动化生成测试用例的难题,为自动驾驶系统的预期功能安全评估提供了有效支持。展开更多
As autonomous driving systems advance rapidly,there is a surge in demand for high-definition(HD)maps that provide accurate and dependable prior information on static environments around vehicles.As one of the main hig...As autonomous driving systems advance rapidly,there is a surge in demand for high-definition(HD)maps that provide accurate and dependable prior information on static environments around vehicles.As one of the main high-level elements in HD maps,the road lane centerline is essential for downstream tasks such as autonomous navigation and planning.Considering the complex topology and significant overlap concerns of road centerlines,previous studies have rarely examined the centerline HD map mapping problem.Recent learningbased pipelines take heuristic post-processing predictions to generate a structured centerline output without instance information.To ameliorate this situation,we propose a novel,end-to-end road centerlines vectorized graph generation pipeline,termed CenterLineFormer.CenterLineFormer takes a single onboard camera image as input and predicts a directed graph representing the lane-layer map in the bird’s-eye view(BEV).We propose a strategy for better view transformation that uses a cross-attention mechanism to generate a dense BEV feature map.With our pipeline,we can describe the connection relationship between different centerlines and generate structured lane graphs for downstream modules as planning and control.Qualitatively,our experiments emphasize that our pipeline achieves a superior performance against previous baselines on nuScenes dataset.We also show that CenterLineFormer can generate accurate centerline graph topologies on night driving and complex traffic intersection scenes.展开更多
文摘随着自动驾驶技术的快速发展,如何保证自动驾驶系统的安全性变得愈发重要,因此预期功能安全(Safety of The Intended Functionality, SOTIF)的概念应运而生,它主要是为了减少由于系统非预期的感知和决策错误而引起的危害。本文提出了一种依托自然驾驶数据的SOTIF自动化生成测试用例的方法。通过采集360°IBEO与环视摄像头数据,分析了4000多个前车切入场景,对关键变量进行参数化建模。采用改进的Monte-Carlo抽样技术,处理独立与非独立随机变量的联合分布,生成覆盖广泛场景的测试用例。实验结果表明该方法显著提升了测试用例生成效率,全面覆盖边角、危险及极端场景,解决了SOTIF测试中自动化生成测试用例的难题,为自动驾驶系统的预期功能安全评估提供了有效支持。
基金the National Key Research and Development Program of China(No.2018YFB1305005)。
文摘As autonomous driving systems advance rapidly,there is a surge in demand for high-definition(HD)maps that provide accurate and dependable prior information on static environments around vehicles.As one of the main high-level elements in HD maps,the road lane centerline is essential for downstream tasks such as autonomous navigation and planning.Considering the complex topology and significant overlap concerns of road centerlines,previous studies have rarely examined the centerline HD map mapping problem.Recent learningbased pipelines take heuristic post-processing predictions to generate a structured centerline output without instance information.To ameliorate this situation,we propose a novel,end-to-end road centerlines vectorized graph generation pipeline,termed CenterLineFormer.CenterLineFormer takes a single onboard camera image as input and predicts a directed graph representing the lane-layer map in the bird’s-eye view(BEV).We propose a strategy for better view transformation that uses a cross-attention mechanism to generate a dense BEV feature map.With our pipeline,we can describe the connection relationship between different centerlines and generate structured lane graphs for downstream modules as planning and control.Qualitatively,our experiments emphasize that our pipeline achieves a superior performance against previous baselines on nuScenes dataset.We also show that CenterLineFormer can generate accurate centerline graph topologies on night driving and complex traffic intersection scenes.
