A method for synthetic LiDAR generation to create annotated datasets for autonomous vehicles perception

Proceedings of: 2019 IEEE Intelligent Transportation Systems Conference (ITSC)LiDAR devices have become a key sensor for autonomous vehicles perception due to their ability to capture reliable geometry information. Indeed, approaches processing LiDAR data have shown an impressive accuracy for 3D object detection tasks, outperforming methods solely based on image inputs. However, the wide diversity of on-board sensor configurations makes the deployment of published algorithms into real platforms a hard task, due to the scarcity of annotated datasets containing laser scans. We present a method to generate new point clouds datasets as captured by a real LiDAR device. The proposed pipeline makes use of multiple frames to perform an accurate 3D reconstruction of the scene in the spherical coordinates system that enables the simulation of the sweeps of a virtual LiDAR sensor, configurable both in location and inner specifications. The similarity between real data and the generated synthetic clouds is assessed through a set of experiments performed using KITTI Depth and Object Benchmarks.Research supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21), and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research

Synthetic Datasets for Autonomous Driving: A Survey

Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their expensive and time-consuming experimental and labeling costs. Therefore, more and more researchers are turning to synthetic datasets to easily generate rich and changeable data as an effective complement to the real world and to improve the performance of algorithms. In this paper, we summarize the evolution of synthetic dataset generation methods and review the work to date in synthetic datasets related to single and multi-task categories for to autonomous driving study. We also discuss the role that synthetic dataset plays the evaluation, gap test, and positive effect in autonomous driving related algorithm testing, especially on trustworthiness and safety aspects. Finally, we discuss general trends and possible development directions. To the best of our knowledge, this is the first survey focusing on the application of synthetic datasets in autonomous driving. This survey also raises awareness of the problems of real-world deployment of autonomous driving technology and provides researchers with a possible solution.Comment: 19 pages, 5 figure

A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed

A Simulation Environment with Reduced Reality Gap for Testing Autonomous Vehicles

In order to facilitate acceptance and ensure safety, autonomous vehicles must be tested not only in typical and relatively safe scenarios but also in dangerous and less frequent scenarios. Recent pedestrian fatalities caused by test vehicles of the front-running giants like Google and Tesla suffice the fact that Autonomous Vehicle technology is not yet mature enough and still needs rigorous exposure to a wide range of traffic, landscape, and natural conditions on which the Autonomous Vehicles can be trained on to perform as expected in real traffic conditions. Simulation Environments have been considered as an efficient, safe, flexible and cost-effective option for the training, testing, and validation of Autonomous Vehicle technology. While ad-hoc task-specific use of simulation in Autonomous Driving research is widespread, simulation platforms that bridge the gap between simulation and reality are limited. This research proposes to set up a highly realistic simulation environment (using CARLA driving simulator) to generate realistic data to be used for Autonomous Driving research. Our system is able to recreate the original traffic scenarios based on prior information about the traffic scene. Furthermore, the system will allow to make changes to the original scenarios and create various desired testing scenarios by varying the parameters of traffic actors, such as location, trajectory, speed, motion states, etc. and hence collect more data with ease

Data-centric Design and Training of Deep Neural Networks with Multiple Data Modalities for Vision-based Perception Systems

224 p.Los avances en visión artificial y aprendizaje automático han revolucionado la capacidad de construir sistemas que procesen e interpreten datos digitales, permitiéndoles imitar la percepción humana y abriendo el camino a un amplio rango de aplicaciones. En los últimos años, ambas disciplinas han logrado avances significativos,impulsadas por los progresos en las técnicas de aprendizaje profundo(deep learning). El aprendizaje profundo es una disciplina que utiliza redes neuronales profundas (DNNs, por sus siglas en inglés) para enseñar a las máquinas a reconocer patrones y hacer predicciones basadas en datos. Los sistemas de percepción basados en el aprendizaje profundo son cada vez más frecuentes en diversos campos, donde humanos y máquinas colaboran para combinar sus fortalezas.Estos campos incluyen la automoción, la industria o la medicina, donde mejorar la seguridad, apoyar el diagnóstico y automatizar tareas repetitivas son algunos de los objetivos perseguidos.Sin embargo, los datos son uno de los factores clave detrás del éxito de los algoritmos de aprendizaje profundo. La dependencia de datos limita fuertemente la creación y el éxito de nuevas DNN. La disponibilidad de datos de calidad para resolver un problema específico es esencial pero difícil de obtener, incluso impracticable,en la mayoría de los desarrollos. La inteligencia artificial centrada en datos enfatiza la importancia de usar datos de alta calidad que transmitan de manera efectiva lo que un modelo debe aprender. Motivada por los desafíos y la necesidad de los datos, esta tesis formula y valida cinco hipótesis sobre la adquisición y el impacto de los datos en el diseño y entrenamiento de las DNNs.Específicamente, investigamos y proponemos diferentes metodologías para obtener datos adecuados para entrenar DNNs en problemas con acceso limitado a fuentes de datos de gran escala. Exploramos dos posibles soluciones para la obtención de datos de entrenamiento, basadas en la generación de datos sintéticos. En primer lugar, investigamos la generación de datos sintéticos utilizando gráficos 3D y el impacto de diferentes opciones de diseño en la precisión de los DNN obtenidos. Además, proponemos una metodología para automatizar el proceso de generación de datos y producir datos anotados variados, mediante la replicación de un entorno 3D personalizado a partir de un archivo de configuración de entrada. En segundo lugar, proponemos una red neuronal generativa(GAN) que genera imágenes anotadas utilizando conjuntos de datos anotados limitados y datos sin anotaciones capturados en entornos no controlados

Switching GAN-based Image Filters to Improve Perception for Autonomous Driving

Autonomous driving holds the potential to increase human productivity, reduce accidents caused by human errors, allow better utilization of roads, reduce traffic accidents and congestion, free up parking space and provide many other advantages. Perception of Autonomous Vehicles (AV) refers to the use of sensors to perceive the world, e.g. using cameras to detect and classify objects. Traffic scene understanding is a key research problem in perception in autonomous driving, and semantic segmentation is a useful method to address this problem. Adverse weather conditions are a reality that AV must contend with. Conditions like rain, snow, haze, etc. can drastically reduce visibility and thus affect computer vision models. Models for perception for AVs are currently designed for and tested on predominantly ideal weather conditions under good illumination. The most complete solution may be to have the segmentation networks be trained on all possible adverse conditions. Thus a dataset to train a segmentation network to make it robust to rain would need to have adequate data that cover these conditions well. Moreover, labeling is an expensive task. It is particularly expensive for semantic segmentation, as each object in a scene needs to be identified and each pixel annotated in the right class. Thus, the adverse weather is a challenging problem for perception models in AVs. This thesis explores the use of Generative Adversarial Networks (GAN) in order to improve semantic segmentation. We design a framework and a methodology to evaluate the proposed approach. The framework consists of an Adversity Detector, and a series of denoising filters. The Adversity Detector is an image classifier that takes as input clear weather or adverse weather scenes, and attempts to predict whether the given image contains rain, or puddles, or other conditions that can adversely affect semantic segmentation. The filters are denoising generative adversarial networks that are trained to remove the adverse conditions from images in order to translate the image to a domain the segmentation network has been trained on, i.e. clear weather images. We use the prediction from the Adversity Detector to choose which GAN filter to use. The methodology we devise for evaluating our approach uses the trained filters to output sets of images that we can then run segmentation tasks on. This, we argue, is a better metric for evaluating the GANs than similarity measures such as SSIM. We also use synthetic data so we can perform systematic evaluation of our technique. We train two kinds of GANs, one that uses paired data (CycleGAN), and one that does not (Pix2Pix). We have concluded that GAN architectures that use unpaired data are not sufficiently good models for denoising. We train the denoising filters using the other architecture and we found them easy to train, and they show good results. While these filters do not show better performance than when we train our segmentation network with adverse weather data, we refer back to the point that training the segmentation network requires labelled data which is expensive to collect and annotate, particularly for adverse weather and lighting conditions. We implement our proposed framework and report a 17\% increase in performance in segmentation over the baseline results obtained when we do not use our framework