Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments

基于时空概率占用热力图的自动驾驶车辆动态风险评估

Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps

本文提出概率占用风险评估（PORA）指标，利用时空热力图预测周围交通参与者的概率占用，结合Cox模型调整相对运动风险，提升自动驾驶车辆动态交通场景碰撞风险评估准确性，且在蒙特卡洛模拟中表现优于其他安全替代指标。

Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment

In a context of autonomous robots, one of the most important task is to ensure the safety of the robot and its surrounding. Most of the time, the risk of navigation is simply said to be the probability of collision. This notion of risk is not well defined in the literature, especially when dealing with occupancy grids. The Bayesian occupancy grid is the most used method to deal with complex environments. However, this is not fitted to compute the risk along a path by its discrete nature, hence giving poor results. In this article, we present a new way to store the occupancy of the environment …

UniOcc：自动驾驶中用于占用预测与预报的统一基准

UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving

本文提出 UniOcc，一个用于自动驾驶中占用预报与预测的统一基准，整合多源真实与仿真数据并提供 2D/3D 占用标签及逐体素流，同时引入无监督评估指标，实验证明大规模多样数据与显式流信息可显著提升模型性能。

A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited…

Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments

Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from demonstrations with on-site collected trajectory data is a powerful approach to discover recurrent motion patterns, generalization to new environments, where sufficient motion data are not readily available, remains a challenge. In many cases, however, semantic information about the environment is a highly informative cue for the prediction of pedestrian motion or the estimation of collision risks. In this work, we …

Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning

Micro Aerial Vehicles (MAVs) that operate in unstructured, unexplored environments require fast and flexible local planning, which can replan when new parts of the map are explored. Trajectory optimization methods fulfill these needs, but require obstacle distance information, which can be given by Euclidean Signed Distance Fields (ESDFs). We propose a method to incrementally build ESDFs from Truncated Signed Distance Fields (TSDFs), a common implicit surface representation used in computer graphics and vision. TSDFs are fast to build and smooth out sensor noise over many observations, and are…