End-to-End Learning of Semantic Grid Estimation Deep Neural Network with Occupancy Grids

International audienceWe propose semantic grid, a spatial 2D map of the environment around an autonomous vehicle consisting of cells which represent the semantic information of the corresponding region such as car, road, vegetation, bikes, etc. It consists of an integration of an occupancy grid, which computes the grid states with a Bayesian filter approach, and semantic segmentation information from monocular RGB images, which is obtained with a deep neural network. The network fuses the information and can be trained in an end-to-end manner. The output of the neural network is refined with a conditional random field. The proposed method is tested in various datasets (KITTI dataset, Inria-Chroma dataset and SYNTHIA) and different deep neural network architectures are compared

WILL YOU CARRY THAT WATCH? INVESTIGATING FACTORS THAT AFFECT CONTINUANCE INTENTION OF SMARTWATCHES

The interest in wearable technologies, especially smartwatches rise day by day parallel with technological developments and an increasing need to monitor health. In line with those developments, this study aims to investigate the role of perceived ease of use, perceived usefulness, user satisfaction, healthology in explaining smartwatch continuance intention. In addition, this study investigates the relationships between perceived ease of use, perceived usefulness, healthology and user satisfaction. Questionnaire method was used to gather data from actual smartwatch consumers in Turkey and the data analyzed by utilizing structural equation modeling. Findings demonstrate that the most powerful variable to explain smartwatch continuance intention is perceived usefulness, whereas perceived ease of use contributes to user satisfaction the most. Also, healthology is positively related to both user satisfaction and continuance intention. The results also highlight the importance of continuance intention to increase intention to recommend smartwatches to other people

Semantic Segmentation with Unsupervised Domain Adaptation Under Varying Weather Conditions for Autonomous Vehicles

International audienceSemantic information provides a valuable source for scene understanding around autonomous vehicles in order to plan their actions and make decisions; however, varying weather conditions reduce the accuracy of the semantic segmentation. We propose a method to adapt to varying weather conditions without supervision, namely without labeled data. We update the parameters of a deep neural network (DNN) model that is pre-trained on the known weather condition (source domain) to adapt it to the new weather conditions (target domain) without forgetting the segmentation in the known weather condition. Furthermore, we don't require the labels from the source domain during adaptation training. The parameters of the DNN are optimized to reduce the distance between the distribution of the features from the images of old and new weather conditions. To measure this distance, we propose three alternatives: W-GAN, GAN and maximum-mean discrepancy (MMD). We evaluate our method on various datasets with varying weather conditions. The results show that the accuracy of the semantic segmentation is improved for varying conditions after adaptation with the proposed method

YOLO-based Panoptic Segmentation Network

International audienceAutonomous vehicles need information about their surroundings to safely navigate them. For this, the task of Panoptic Segmentation is proposed as a method of fully parsing the scene by assigning each pixel a label and instance id. Given the constraints of autonomous driving, this process needs to be done in a fast manner. In this paper, we propose the first panoptic segmentation network based on the YOLOv3 real-time object detection network by adding a semantic and instance segmentation branches. YOLO-panoptic is able to do real-time inference and achieves a performance similar to the state of the art methods in some metrics

Semantic Grid Estimation with Occupancy Grids and Semantic Segmentation Networks

International audienceWe propose a method to estimate the semantic grid for an autonomous vehicle. The semantic grid is a 2D bird's eye view map where the grid cells contain semantic characteristics such as road, car, pedestrian, signage, etc. We obtain the semantic grid by fusing the semantic segmentation information and an occupancy grid computed by using a Bayesian filter technique. To compute the semantic information from a monocular RGB image, we integrate segmentation deep neural networks into our model. We use a deep neural network to learn the relation between the semantic information and the occupancy grid which can be trained end-to-end extending our previous work on semantic grids. Furthermore, we investigate the effect of using a conditional random field to refine the results. Finally, we test our method on two datasets and compare different architecture types for semantic segmentation. We perform the experiments on KITTI dataset and Inria-Chroma dataset

Recognize Moving Objects Around an Autonomous Vehicle Considering a Deep-learning Detector Model and Dynamic Bayesian Occupancy

International audiencePerception systems on autonomous vehicles have the challenge of understanding the traffic scene in different situations. The fusion of redundant information obtained from different sources has been shown considerable progress under different methodologies to achieve this objective. However, new opportunities are available to obtain better fusion results with the advance of deep-learning models and computing hardware. In this paper, we aim to recognize moving objects in traffic scenes through the fusion of semantic information with occupancy-grid estimations. Our approach considers a deep-learning model with inference times between 22 to 55 milliseconds. Moreover, we use a Bayesian occupancy framework with a Highly-parallelized design to obtain the occupancygrid estimations.We validate our approach using experimental results with real-world data on urban scenery

GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids

International audienceMultiple Object Tracking is an important task for autonomous vehicles. However, it gets difficult to track objects when it is hard to detect them due to occlusion or distance to the sensors. We propose a method, "GridTrack", to overcome this difficulty. We fuse a dynamic occupancy grid map (DOGMa) with an object detector. DOGMa is obtained by applying a Bayesian filter on raw sensor data. This improves the tracking of the partially observed / unobserved objects with the help of the Bayesian filter on raw data, which has a powerful prediction capability. We develop a network to track the objects on the grid and fuse information from previous detections in this network. The experiments show that the multi-object tracking accuracy is high with the usage of the proposed method

Instance Segmentation with Unsupervised Adaptation to Different Domains for Autonomous Vehicles

International audienceDetection of the objects around a vehicle is important for a safe and successful navigation of an autonomous vehicle. Instance segmentation provides a fine and accurate classification of the objects such as cars, trucks, pedestrians, etc. In this study, we propose a fast and accurate approach which can detect and segment the object instances which can be adapted to new conditions without requiring the labels from the new condition. Furthermore, the performance of the instance segmentation does not degrade in detection of the objects in the original condition after it adapts to the new condition. To our knowledge, currently there are not other methods which perform unsupervised domain adaptation for the task of instance segmentation using non-synthetic datasets. We evaluate the adaptation capability of our method on two datasets. Firstly, we test its capacity of adapting to a new domain; secondly, we test its ability to adapt to new weather conditions. The results show that it can adapt to new conditions with an improved accuracy while preserving the accuracy of the original condition

MultiLane: Lane Intention Prediction and Sensible Lane-Oriented Trajectory Forecasting on Centerline Graphs

International audienceForecasting the motion of surrounding traffic is one of the key challenges in the quest to achieve safe autonomous driving technology. Current state-of-the-art deep forecasting architectures are capable of producing impressive results. However, in many cases, they also output completely unreasonable trajectories, making them unsuitable for deployment. In this work, we present a deep forecasting architecture that leverages the map lane centerlines available in recent datasets to predict sensible trajectories; that is, trajectories that conform to the road layout, agree with the observed dynamics of the target, and react to the presence of surrounding agents. To model such sensible behavior, the proposed architecture first predicts the lane or lanes that the target agent is likely to follow. Then, a navigational goal along each candidate lane is predicted, allowing the regression of the final trajectory in a laneand goal-oriented manner. Our experiments in the Argoverse dataset show that our architecture achieves performance onpar with lane-oriented state-of-the-art forecasting approaches and not far behind goal-oriented approaches, while consistently producing sensible trajectories

Attentional PointNet for 3D-Object Detection in Point Clouds

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