Generating evidential BEV maps in continuous driving space

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown. Different from only providing deterministic or probabilistic results, e.g., probabilistic object detection, that only provide partial information for the perception scenario, we propose a complete probabilistic model named GevBEV. It interprets the 2D driving space as a probabilistic Bird's Eye View (BEV) map with point-based spatial Gaussian distributions, from which one can draw evidence as the parameters for the categorical Dirichlet distribution of any new sample point in the continuous driving space. The experimental results show that GevBEV not only provides more reliable uncertainty quantification but also outperforms the previous works on the benchmarks OPV2V and V2V4Real of BEV map interpretation for cooperative perception in simulated and real-world driving scenarios, respectively. A critical factor in cooperative perception is the data transmission size through the communication channels. GevBEV helps reduce communication overhead by selecting only the most important information to share from the learned uncertainty, reducing the average information communicated by 87% with only a slight performance drop. Our code is published at https://github.com/YuanYunshuang/GevBEV

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This work reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlook challenges between current academic research and real-world applications. The project page is https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-DrivingComment: 18 pages, 6 figures. Accepted by IEEE Intelligent Transportation Systems Magazine. URL: https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Drivin

CoBEVFusion: Cooperative Perception with LiDAR-Camera Bird's-Eye View Fusion

Autonomous Vehicles (AVs) use multiple sensors to gather information about their surroundings. By sharing sensor data between Connected Autonomous Vehicles (CAVs), the safety and reliability of these vehicles can be improved through a concept known as cooperative perception. However, recent approaches in cooperative perception only share single sensor information such as cameras or LiDAR. In this research, we explore the fusion of multiple sensor data sources and present a framework, called CoBEVFusion, that fuses LiDAR and camera data to create a Bird's-Eye View (BEV) representation. The CAVs process the multi-modal data locally and utilize a Dual Window-based Cross-Attention (DWCA) module to fuse the LiDAR and camera features into a unified BEV representation. The fused BEV feature maps are shared among the CAVs, and a 3D Convolutional Neural Network is applied to aggregate the features from the CAVs. Our CoBEVFusion framework was evaluated on the cooperative perception dataset OPV2V for two perception tasks: BEV semantic segmentation and 3D object detection. The results show that our DWCA LiDAR-camera fusion model outperforms perception models with single-modal data and state-of-the-art BEV fusion models. Our overall cooperative perception architecture, CoBEVFusion, also achieves comparable performance with other cooperative perception models

Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning. The novelty of this work is threefold: (1) developing a three-stage fusion scheme to predict the number of objects effectively and to fuse multiple local maps with fidelity scores; (2) developing an FL algorithm which fine-tunes feature models (i.e., representation learning networks for feature extraction) distributively by aggregating model parameters; (3) developing a knowledge distillation method to generate FL training labels when data labels are unavailable. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes.Comment: 12 pages, 5 figures, to appear in 2021 IEEE International Conference on Robotics and Automation (ICRA

Collaborative Decision-Making Using Spatiotemporal Graphs in Connected Autonomy

Collaborative decision-making is an essential capability for multi-robot systems, such as connected vehicles, to collaboratively control autonomous vehicles in accident-prone scenarios. Under limited communication bandwidth, capturing comprehensive situational awareness by integrating connected agents' observation is very challenging. In this paper, we propose a novel collaborative decision-making method that efficiently and effectively integrates collaborators' representations to control the ego vehicle in accident-prone scenarios. Our approach formulates collaborative decision-making as a classification problem. We first represent sequences of raw observations as spatiotemporal graphs, which significantly reduce the package size to share among connected vehicles. Then we design a novel spatiotemporal graph neural network based on heterogeneous graph learning, which analyzes spatial and temporal connections of objects in a unified way for collaborative decision-making. We evaluate our approach using a high-fidelity simulator that considers realistic traffic, communication bandwidth, and vehicle sensing among connected autonomous vehicles. The experimental results show that our representation achieves over 100x reduction in the shared data size that meets the requirements of communication bandwidth for connected autonomous driving. In addition, our approach achieves over 30% improvements in driving safety