Results in scenarios with stationary and with moving ego-vehicle.Zoning Districts Zoning Districts Height & Bulk Districts Height & Bulk Districts Special Use Districts Special Use Districts Special Sign Districts Special Sign Districts Sign Programs Sign Programs Legislated Setbacks Legislated Setbacks Coastal Zone Coastal Zone Community Benefit Districts Community Benefit Districts Cultural Heritage Districts Cultural Districts Curb Cuts - PC Section 155(r) Curb Cuts - PC Section 155(r) Curb Cuts - Policy 5.1.2 Curb Cuts - Policy 5.1. That our proposed method for ego-motion compensation leads to comparable Separation of the environment in static and dynamic area. To demonstrate the improved accuracy of velocity estimates and the more robust State-of-the-art particle-based algorithm on a large publicly available dataset In our evaluations, we compare our approach with a In neural network architectures with recurrent layers working on different In order to apply our approach with measurements from a movingĮgo-vehicle, we propose a method for ego-motion compensation that is applicable Temporal information for the robust detection of static and dynamicĮnvironment. Due to the combination ofĬonvolutional and recurrent layers, our approach is capable to use spatial and Lidar measurements of a single time step. Our network is fed with sequences of measurement grid maps, which encode the Occupancy grid map, which divides the vehicle surrounding in cells, eachĬontaining the occupancy probability and a velocity estimate. Therefore, we propose to use a recurrent neural network to predict a dynamic Scenarios the motion of other road participants is of special interest. 1.13 1 star 0.90 From the lesson Module 2: Mapping for Planning The occupancy grid is a discretization of space into fixed-sized cells, each of which contains a probability that it is occupied. In addition to the detection of objects, in complex traffic Download a PDF of the paper titled Dynamic Occupancy Grid Mapping with Recurrent Neural Networks, by Marcel Schreiber and 2 other authors Download PDF Abstract: Modeling and understanding the environment is an essential task forĪutonomous driving.
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