Holistic Grid Fusion Based Stop Line Estimation

利用对抗机器学习攻击自动驾驶智能体：基于CARLA排行榜的全面评估

Attacking Autonomous Driving Agents with Adversarial Machine Learning: A Holistic Evaluation with the CARLA Leaderboard

该研究借助CARLA模拟器与CARLA排行榜，在不修改智能体代码的前提下测试对抗补丁对多类开源自动驾驶智能体的攻击效果，发现部分非ML模块可抵消对抗样本对ML模型的误导作用。

StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving

We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy. A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency. In addition to predicting trajectories, our scene encoder lends itself to predicting whole-scene probabilistic occupancy grids, a complementary output representation suitable for busy urban environments. Occupancy grids allow the AV to reason collectively about the behavior o…

Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning

Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands. These networks are appealing because they replace laborious hand engineered building blocks but their black-box nature makes them difficult to delve in case of failure. Recent works have shown the importance of using an explicit intermediate representation that has the benefits of increasing both the interpretability and the accuracy of networks' decisions. Nonetheless, these camera-based networks reason in camera view where scale is not homogeneous and he…

AdaOcc：自适应分辨率占用预测

AdaOcc: Adaptive-Resolution Occupancy Prediction

提出自适应分辨率多模态预测方法AdaOcc，将对象中心3D重建与整体占用预测集成，仅在感兴趣区域进行高精度点云重建，在nuScenes上显著提升近距离场景的IOU和Hausdorff距离指标。

Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation

We propose a novel approach to 3D human pose estimation from a single depth map. Recently, convolutional neural network (CNN) has become a powerful paradigm in computer vision. Many of computer vision tasks have benefited from CNNs, however, the conventional approach to directly regress 3D body joint locations from an image does not yield a noticeably improved performance. In contrast, we formulate the problem as estimating per-voxel likelihood of key body joints from a 3D occupancy grid. We argue that learning a mapping from volumetric input to volumetric output with 3D convolution consistent…

面向整体零样本视频异常分析的统一推理框架

A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis

提出一个统一推理框架，通过链式测试时推理连接时间检测、空间定位与文本解释，在无需额外训练的情况下实现整体零样本视频异常分析，并在多个基准上取得最先进性能。