Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications

Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering

Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this gap by developing a multi-object tracking algorithm that takes an image as input and produces trajectories of detected objects in a world coordinate system. We solve this by using a deep neural network trained to detect and estimate the distance to objects from a single input image. The detections from a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli mixture tracking filter. The combination of the learned detector and the PMBM filter results in an algorithm that achieves 3D tracking using only mono-camera images as input. The performance of the algorithm is evaluated both in 3D world coordinates, and 2D image coordinates, using the publicly available KITTI object tracking dataset. The algorithm shows the ability to accurately track objects, correctly handle data associations, even when there is a big overlap of the objects in the image, and is one of the top performing algorithms on the KITTI object tracking benchmark. Furthermore, the algorithm is efficient, running on average close to 20 frames per second.Comment: 8 pages, 2 figures, for associated videos, see https://goo.gl/Aoydg

Guillain-Barre syndrome after SARS-CoV-2 infection in an international prospective cohort study

In the wake of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, an increasing number of patients with neurological disorders, including Guillain-Barre syndrome (GBS), have been reported following this infection. It remains unclear, however, if these cases are coincidental or not, as most publications were case reports or small regional retrospective cohort studies. The International GBS Outcome Study is an ongoing prospective observational cohort study enrolling patients with GBS within 2 weeks from onset of weakness. Data from patients included in this study, between 30 January 2020 and 30 May 2020, were used to investigate clinical and laboratory signs of a preceding or concurrent SARS-CoV-2 infection and to describe the associated clinical phenotype and disease course. Patients were classified according to the SARS-CoV-2 case definitions of the European Centre for Disease Prevention and Control and laboratory recommendations of the World Health Organization. Forty-nine patients with GBS were included, of whom eight (16%) had a confirmed and three (6%) a probable SARS-CoV-2 infection. Nine of these 11 patients had no serological evidence of other recent preceding infections associated with GBS, whereas two had serological evidence of a recent Campylobacter jejuni infection. Patients with a confirmed or probable SARS-CoV-2 infection frequently had a sensorimotor variant 8/11 (73%) and facial palsy 7/11 (64%). The eight patients who underwent electrophysiological examination all had a demyelinating subtype, which was more prevalent than the other patients included in the same time window [14/30 (47%), P = 0.012] as well as historical region and age-matched control subjects included in the International GBS Outcome Study before the pandemic [23/44 (52%), P = 0.016]. The median time from the onset of infection to neurological symptoms was 16 days (interquartile range 12-22). Patients with SARS-CoV-2 infection shared uniform neurological features, similar to those previously described in other post-viral GBS patients. The frequency (22%) of a preceding SARS-CoV-2 infection in our study population was higher than estimates of the contemporaneous background prevalence of SARS-CoV-2, which may be a result of recruitment bias during the pandemic, but could also indicate that GBS may rarely follow a recent SARS-CoV-2 infection. Consistent with previous studies, we found no increase in patient recruitment during the pandemic for our ongoing International GBS Outcome Study compared to previous years, making a strong relationship of GBS with SARS-CoV-2 unlikely. A case-control study is required to determine if there is a causative link or not

Relativistic Brownian Motion

Stimulated by experimental progress in high energy physics and astrophysics, the unification of relativistic and stochastic concepts has re-attracted considerable interest during the past decade. Focusing on the framework of special relativity, we review, here, recent progress in the phenomenological description of relativistic diffusion processes. After a brief historical overview, we will summarize basic concepts from the Langevin theory of nonrelativistic Brownian motions and discuss relevant aspects of relativistic equilibrium thermostatistics. The introductory parts are followed by a detailed discussion of relativistic Langevin equations in phase space. We address the choice of time parameters, discretization rules, relativistic fluctuation-dissipation theorems, and Lorentz transformations of stochastic differential equations. The general theory is illustrated through analytical and numerical results for the diffusion of free relativistic Brownian particles. Subsequently, we discuss how Langevin-type equations can be obtained as approximations to microscopic models. The final part of the article is dedicated to relativistic diffusion processes in Minkowski spacetime. Due to the finiteness of velocities in relativity, nontrivial relativistic Markov processes in spacetime do not exist; i.e., relativistic generalizations of the nonrelativistic diffusion equation and its Gaussian solutions must necessarily be non-Markovian. We compare different proposals that were made in the literature and discuss their respective benefits and drawbacks. The review concludes with a summary of open questions, which may serve as a starting point for future investigations and extensions of the theory.Comment: review article, 159 pages, references updated, misprints corrected, App. A.4. correcte

Qually: A Quality Validation Toolbox for Automotive Perception Data Towards Trustworthy AI

Data-driven techniques such as artificial intelligence (AI) and deep learning are frequently deployed as part of automotive perception systems. Due to their heavy dependency on data, data quality is at the essence. In particular, in an automotive perception system, data is captured by sensors and transformed into different formats depending on where it is in the AI data processing pipeline. Although data at different stages share similar attributes, the impact of their properties at each individual stage differ significantly from one another. Therefore, data quality requirements need to be defined specifically at each stage.In this project, the objective is to develop an end-to-end quality control toolbox to detect errors and anomalies throughout the entire pipeline. To achieve this objective, we divide the project into three work packages, where the first step is to design a set of data properties and their corresponding requirements as quality specifications for data at each stage. Given these specifications, as a second step, we have developed a toolbox, Qually, to evaluate data quality metrics and detect errors and anomalies throughout the AI pipeline. In the last work package, as a demonstrator, Qually is applied to improve automated annotations. This is implemented in three steps: 1) errors are identified using the quality metrics evaluated by Qually; 2) Qually suggests an automatic correction using ensemble techniques; 3) the corrected annotations are evaluated by Qually to confirm the improvement in quality. The error detection and suggested corrections are manually inspected to statistically validate the outcome of Qually.As the next step, besides further developing Qually as a software to improve its robustness, capacity, scalability and completeness, we plan to focus on enriching the set of data properties and quality specifications, especially by including technical and business requirements from various automotive stakeholders. We also plan to investigate the possibility and scalability of integrating formal verification techniques for quality control

Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering

Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this gap by developing a multi-object tracking algorithm that takes an image as input and produces trajectories of detected objects in a world coordinate system. We solve this by using a deep neural network trained to detect and estimate the distance to objects from a single input image. The detections from a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli mixture tracking filter. The combination of the learned detector and the PMBM filter results in an algorithm that achieves 3D tracking using only mono-camera images as input. The performance of the algorithm is evaluated both in 3D world coordinates, and 2D image coordinates, using the publicly available KITTI object tracking dataset. The algorithm shows the ability to accurately track objects, correctly handle data associations, even when there is a big overlap of the objects in the image, and is one of the top performing algorithms on the KITTI object tracking benchmark. Furthermore, the algorithm is efficient, running on average close to 20 frames per second