Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation. Common challenges in the prediction include forecasting the relative position of other vehicles, modelling the dynamics of vehicles subjected to different traffic conditions, and vanishing surrounding objects. To tackle these challenges, we propose a spatio-temporal prediction network pipeline that takes the past information from the environment and semantic labels separately for generating future occupancy predictions. Compared to the current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds and in a relatively complex environment from the nuScenes dataset. Our experimental results demonstrate the ability of spatio-temporal networks to understand scene dynamics without the need for HD-Maps and explicit modeling dynamic objects. We publicly release our occupancy grid dataset based on nuScenes to support further research.
Detection and segmentation of moving obstacles, along with prediction of the future occupancy states of the local environment, are essential for autonomous vehicles to proactively make safe and informed decisions. In this paper, we propose a framework that integrates the two capabilities together using deep neural network architectures. Our method first detects and segments moving objects in the scene, and uses this information to predict the spatiotemporal evolution of the environment around autonomous vehicles. To address the problem of direct integration of both static-dynamic object segmen…
MetaOcc: Spatio-Temporal Fusion of Surround-View 4D Radar and Camera for 3D Occupancy Prediction with Dual Training Strategies
School of Automotive Studies, Tongji University（同济大学汽车学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Automobile, Chang'an University（长安大学汽车学院）；School of Information and Electrical Engineering, Hangzhou City University（杭州城市学院信息与电气工程学院）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）
Predicting future behaviors of road agents is a key task in autonomous driving. While existing models have demonstrated great success in predicting marginal agent future behaviors, it remains a challenge to efficiently predict consistent joint behaviors of multiple agents. Recently, the occupancy flow fields representation was proposed to represent joint future states of road agents through a combination of occupancy grid and flow, which supports efficient and consistent joint predictions. In this work, we propose a novel occupancy flow fields predictor to produce accurate occupancy and flow p…
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…
WildOcc: A Benchmark for Off-Road 3D Semantic Occupancy Prediction
Research Center for Intelligent Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能计算系统研究中心）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
Institute of Measurement, Control, and Microtechnology, Ulm University（乌尔姆大学测量、控制与微技术研究所）
We tackle the long-term prediction of scene evolution in a complex downtown scenario for automated driving based on Lidar grid fusion and recurrent neural networks (RNNs). A bird's eye view of the scene, including occupancy and velocity, is fed as a sequence to a RNN which is trained to predict future occupancy. The nature of prediction allows generation of multiple hours of training data without the need of manual labeling. Thus, the training strategy and loss function is designed for long sequences of real-world data (unbalanced, continuously changing situations, false labels, etc.). The dee…
Trajectory modelling had been the principal research area for understanding and anticipating human behaviour. Predicting the dynamic path by observing the agent and its surrounding environment are essential for applications such as autonomous driving and indoor navigation suggestions. However, despite the numerous researches that had been presented, most available dataset does not contains any information on environmental factors---such as the occupancy representation of the map---which arguably plays a significant role on how an agent chooses its trajectory. We present a trajectory dataset wi…
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications
Shanghai Jiao Tong University（上海交通大学）；HAOMO.AI Technology Co., Ltd.（HAOMO.AI技术有限公司）；National University of Defense Technology（国防科技大学）；Beijing Institute of Technology（北京理工大学）
Uber ATG（优步先进技术集团）；University of Toronto（多伦多大学）；Cornell University（康奈尔大学）
In this paper, we address the important problem in self-driving of forecasting multi-pedestrian motion and their shared scene occupancy map, critical for safe navigation. Our contributions are two-fold. First, we advocate for predicting both the individual motions as well as the scene occupancy map in order to effectively deal with missing detections caused by postprocessing, e.g., confidence thresholding and non-maximum suppression. Second, we propose a Scene-Actor Graph Neural Network (SA-GNN) which preserves the relative spatial information of pedestrians via 2D convolution, and captures th…
InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanyang Technological University（南洋理工大学）；Huawei Technologies Ltd.（华为技术有限公司）；University of New South Wales（新南威尔士大学）
Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter in the real world. To combat this deficiency, we propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road ac…
Monocular Occupancy Prediction for Scalable Indoor Scenes
School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；Centre for Artificial Intelligence and Robotics (HKISI_CAS)（人工智能与机器人中心）
OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model
The School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Waytous Inc.（Waytous公司）；The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；The College of Surveying and Geo-Informatics, Tongji University（同济大学测绘与地理信息学院）
Safe and proactive planning in robotic systems generally requires accurate predictions of the environment. Prior work on environment prediction applied video frame prediction techniques to bird's-eye view environment representations, such as occupancy grids. ConvLSTM-based frameworks used previously often result in significant blurring and vanishing of moving objects, thus hindering their applicability for use in safety-critical applications. In this work, we propose two extensions to the ConvLSTM to address these issues. We present the Temporal Attention Augmented ConvLSTM (TAAConvLSTM) and S…
Pedestrian trajectory prediction is essential for collision avoidance in autonomous driving and robot navigation. However, predicting a pedestrian's trajectory in crowded environments is non-trivial as it is influenced by other pedestrians' motion and static structures that are present in the scene. Such human-human and human-space interactions lead to non-linearities in the trajectories. In this paper, we present a new spatio-temporal graph based Long Short-Term Memory (LSTM) network for predicting pedestrian trajectory in crowded environments, which takes into account the interaction with st…
Johns Hopkins University Applied Physics Lab（约翰斯·霍普金斯大学应用物理实验室）；Johns Hopkins University（约翰斯·霍普金斯大学）
Fast, collision-free motion through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV). By contrast, biological systems routinely make decisions by taking into consideration what might exist beyond their FOV based on prior experience. In this paper, we present an approach for predicting occupancy map representations of sensor data for future robot motions using deep neural networks. We evaluate several deep network architectures, including pure…
HiMAP: History-aware Map-occupancy Prediction with Fallback
Institute of Cartography and Geoinformatics, Leibniz University Hannover（莱比锡大学汉诺威分校制图与地理信息学研究所）；Institute of Information Processing, Leibniz University Hannover（莱比锡大学汉诺威分校信息处理研究所）；Faculty of Geo-Information Science and Earth Observation, University of Twente（埃因霍温大学地球信息科学与地球观测系）
LASTIG, Univ. Gustave Eiffel, IGN-ENSG（古斯塔夫·埃菲尔大学LASTIG实验室，法国国家地理与林业信息学院）；INRAE, UMR 1202 BIOGECO, Université de Bordeaux（法国国家农业食品与环境研究院，波尔多大学生物地球化学与生态学联合研究中心）；EA G&E Bordeaux INP, Université Bordeaux Montaigne（波尔多蒙田大学，波尔多国立高等电子、计算机、电信、数学与机械学院环境与地理空间工程研究团队）
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cover. Our weakly-supervised training scheme allows our network to only be supervised with vegetation occupancy values aggregated over cylindrical plots containing thousands of points. Such ground truth is easier to produce than pixel-wise or point-wise annotations. Our method outperforms handcrafted and deep learning baselines in terms of precision b…
Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）；Beijing Institute of Technology（北京理工大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Autonomous vehicles commonly rely on highly detailed birds-eye-view maps of their environment, which capture both static elements of the scene such as road layout as well as dynamic elements such as other cars and pedestrians. Generating these map representations on the fly is a complex multi-stage process which incorporates many important vision-based elements, including ground plane estimation, road segmentation and 3D object detection. In this work we present a simple, unified approach for estimating maps directly from monocular images using a single end-to-end deep learning architecture. F…
