ADS-Lead: Lifelong Anomaly Detection in Autonomous Driving Systems

Key stakeholder perceived value’s influence on autonomous vehicles’ privacy and security governance – an evolutionary analysis based on the prospect theory

“…Enterprises, driven by their quest to advance technologies, proactively establish data platforms. However, these platforms also give rise to potential risks, such as privacy breaches and ethical obligations (Han et al , 2023). Consequently, all parties are actively urging enterprises to further fulfill their responsibilities.…”

Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems

“…Additionally, incremental learning has been explored in different domains, including image classification (Meng et al, 2022;Nguyen et al, 2022;Zhao et al, 2022), natural language processing (Jan Moolman Buys University College University of Oxford, 2017; Kahardipraja et al, 2023), recommender systems (Ouyang et al, 2021;Wang et al, 2021;Ahrabian et al, 2021a), and data stream mining (Eisa et al, 2022). Researchers have investigated different strategies such as incremental decision trees (Barddal and Fabr'ıcio Enembreck., 2020;Choyon et al, 2020;Han et al, 2023), online clustering (Bansiwala et al, 2021), ensemble methods (Lovinger and Valova, 2020;Zhang J. et al, 2023), and deep learning approaches to tackle incremental learning problems (Ali et al, 2022). Incremental learning enables lifelong learning to constantly learn new data new data while leveraging prior knowledge that continues to be an active research topic (Figure 1).…”

“…The development of autonomous vehicles has advanced quickly in recent years (Han et al, 2023). Modern vehicles are becoming more and more automated and intelligent due to advancements in lifelong learning algorithms, mechanical, and computing technologies (Su et al, 2012).…”

“…Particularly, in the field of autonomous driving, an effective collaborative anomaly detection methodology known as ADS-Lead was proposed to safeguard the lane-following mechanism of ADSs. It has a unique transformer-based one-class classification algorithm to detect adversarial image examples (traffic sign and lane identification threats) as well as time series anomalies (GPS spoofing threat) (Han et al, 2023). In addition, to enhance the anomaly detection performance of models, an active lifetime anomaly detection framework was provided for class-incremental scenarios that supports any memory-based experience replay mechanism, any query strategy, and any anomaly detection model (Faber et al, 2022).…”

自动驾驶开源数据体系：现状与未来

“…Front-view Image Laneline PV VPG [76] 2017 -20K/20K PV -TUsimple [77] 2017 6.4K 6.4K/128K PV CULane [78] 2018 -133K/133K PV -ApolloScape [14] 2018 235 115K/115K PV LLAMAS [79] 2019 14 79K/100K PV 3D Synthetic [80] 2020 -10K/10K PV -CurveLanes [81] 2020 -150K/150K PV -VIL-100 [82] 2021 100 10K/10K PV OpenLane-V1 [83] 2022 1K 200K/200K 3D ONCE-3DLane [84] 2022 -211K/211K 3D -OpenLane-V2 [85] 。交通灯检测数据集可以被视为一种特定类别的图像 检测数据集。初始的车道线检测数据集 [14, 75∼82] 在二维图像坐标系中检测车道线，然后通过逆透视 变换(Inverse Perspective Mapping，IPM)投影矩阵获得三维车道线。由于 IPM 算法基于路面符 合平面假设的设定，而现实中大多数路面存在高度变化，导致在透视图中表示的车道线在投影到三 维空间的过程中容易出现错误。为解决这个问题，近几年的车道线数据集 [83,84] 提出直接进行三维 车道线检测的任务。由于车道线并不是车道的完备表达，无法包含车道方向与车道之间的连接等关 系，进一步地，OpenLane-V2 [85] 引入了车道的实例级表达方式，并且通过拓扑关系的构建赋予其 连接性及其与交通标识的关联性。建图类数据集的发展使模型预测结果所包含的信息越来越接近高 精地图。 Argoverse [16] [137] , [138] , [139] nuScenes [8] [140] , [141] , [142] Waymo [9] [143] , [144] , [145] Interaction [146] [147] , [148] , [149] MONA [150] Trajectory Comfort nuPlan [18] [151] , [152] , [153] CARLA [30] [154] , [155] , [156] MetaDrive [157] [158] , [159] , [160] Apollo [161] [162] , [163] , [164] Path Planning Maps for Road Network Routes Connecting to Nod...…”