In recent years, prior maps have become a mainstream tool in autonomous navigation. However, commonly available prior maps are still tailored to control-and-decision tasks, and the use of these maps for localization remains largely unexplored. To bridge this gap, we propose a lidar-based localization and mapping (LOAM) system that can exploit the common HD-maps in autonomous driving scenarios. Specifically, we propose a technique to extract information from the drivable area and ground surface height components of the HD-maps to construct 4DOF pose priors. These pose priors are then further integrated into the pose-graph optimization problem to create a globally consistent 3D map. Experiments show that our scheme can significantly improve the global consistency of the map compared to state-of-the-art lidar-only approaches, proven to be a useful technology to enhance the system's robustness, especially in GPS-denied environment. Moreover, our work also serves as a first step towards long-term navigation of robots in familiar environment, by updating a map. In autonomous driving this could enable updating the HD-maps without sourcing a new from a third party company, which is expensive and introduces delays from change in the world to updated map.

The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annotated high-definition (HD) maps. Recent studies show that these models can greatly benefit from increasing the amount of human data available for training. However, the manual annotation of HD maps which is necessary for every new location puts a bottleneck on efficiently scaling up human traffic datasets. We propose an aerial image-based map (AIM) representation that requires minimal annotation and provides rich road context information for traffic agents like pedestrians and vehicles. We evaluate multi-agent trajectory prediction using the AIM by incorporating it into a differentiable driving simulator as an image-texture-based differentiable rendering module. Our results demonstrate competitive multi-agent trajectory prediction performance especially for pedestrians in the scene when using our AIM representation as compared to models trained with rasterized HD maps.

Across all experiments, Hyperseed convincingly machine learning and robotics context is currently gaining a demonstrates its key novelties of learning from a few input great momentum [1]-[6]. In classification tasks, the use of vectors and single vector operation learning rule, both of which VSA leads to order of magnitude increase in energy efficiency contribute towards reduced time and computation complexity. of computations on the one hand and natively enables oneshot The paper is structured as follows. Section II describes and multitask learning on the other [7]. It is prospected the related work relevant to Hyperseed operations. The used that VSA will play a key role in the development of novel methods including the fundamentals of VSA are presented neuromorphic computer architectures [8] as an algorithmic in Section III. Section IV presents the main contribution - abstraction [9], [10]. The main contribution of this paper is the method for unsupervised learning Hyperseed. Section V a novel algorithm for unsupervised learning called Hyperseed, reports the results of the performance evaluation the experiments.

This video shows our Driving Intelligence completing an unprotected right turn through an intersection near our London King's Cross HQ. This is one of the hardest manoeuvres for autonomy and behaviour Wayve has been able to learn with end-to-end deep learning. Unlike other approaches, we learn to drive from data using camera-first sensing without needing an HD-map. We train our system to understand the world around it with computer vision and learn to drive with imitation and reinforcement learning. In this example, our Driving Intelligence is able to navigate the complex lane layout, avoiding the car which runs the red light and passing the pedestrians with human-like confidence.