Bayesian Road Estimation Using Onboard Sensors

This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors,and an inertial measurement unit.We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a Bayesian fusionsystem that uses the following information from the surroundings: lane marking measurements obtained by the camera and leading vehicle and stationary object measurements obtained bya radar–camera fusion system. The performance of our fusion algorithm is evaluated in several drive tests. As expected, the more information we use, the better the performance is.Index Terms—Camera, information fusion, radar, road geometry,unscented Kalman filter (UKF)

Poisson Multi-Bernoulli Mapping Using Gibbs Sampling

This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multi-object posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multi-object posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform a state-of-the-art method.Comment: 14 pages, 6 figure

Variational Bayesian Expectation Maximization for Radar Map Estimation

Abstract-For self-localization, a detailed and reliable map of the environment can be used to relate sensor data to static features with known locations. This paper presents a method for construction of detailed radar maps that describe the expected intensity of detections. Specifically, the measurements are modelled by an inhomogeneous Poisson process with a spatial intensity function given by the sum of a constant clutter level and an unnormalized Gaussian mixture. A substantial difficulty with radar mapping is the presence of data association uncertainties, i.e., the unknown associations between measurements and landmarks. In this paper, the association variables are introduced as hidden variables in a variational Bayesian expectation maximization (VBEM) framework, resulting in a computationally efficient mapping algorithm that enables a joint estimation of the number of landmarks and their parameters

Fractures, Bone Mineral Density, and Final Height in Craniopharyngioma Patients with a Follow-up of 16 Years

CONTEXT: Pituitary hormonal deficiencies in patients with craniopharyngioma may impair their bone health. OBJECTIVE: To investigate bone health in patients with craniopharyngioma. DESIGN: Retrospective cross-sectional study. SETTING: Dutch and Swedish referral centers. PATIENTS: Patients with craniopharyngioma (n = 177) with available data on bone health after a median follow-up of 16 years (range, 1-62) were included (106 [60%] Dutch, 93 [53%] male, 84 [48%] childhood-onset disease). MAIN OUTCOME MEASURES: Fractures, dual X-ray absorptiometry-derived bone mineral density (BMD), and final height were evaluated. Low BMD was defined as T- or Z-score ≤-1 and very low BMD as ≤-2.5 or ≤-2.0, respectively. RESULTS: Fractures occurred in 31 patients (18%) and were more frequent in men than in women (26% vs. 8%, P = .002). Mean BMD was normal (Z-score total body 0.1 [range, -4.1 to 3.5]) but T- or Z-score ≤-1 occurred in 47 (50%) patients and T-score ≤-2.5 or Z-score ≤-2.0 in 22 (24%) patients. Men received less often treatment for low BMD than women (7% vs. 18%, P = .02). Female sex (OR 0.3, P = .004) and surgery (odds ratio [OR], 0.2; P = .01) were both independent protective factors for fractures, whereas antiepileptic medication was a risk factor (OR, 3.6; P = .03), whereas T-score ≤-2.5 or Z-score ≤-2.0 was not (OR, 2.1; P = .21). Mean final height was normal and did not differ between men and women, or adulthood and childhood-onset patients. CONCLUSIONS: Men with craniopharyngioma are at higher risk than women for fractures. In patients with craniopharyngioma, a very low BMD (T-score ≤-2.5 or Z-score ≤-2.0) seems not to be a good predictor for fracture risk

Road geometry estimation using a precise clothoid road model and observations of moving vehicles

An important part of any advanced driver assistance system is road geometry estimation. In this paper, we develop a Bayesian estimation algorithm using lane marking measurements received from a camera and measurements of the leading vehicles received from a radar-camera fusion system, to estimate the road up to 200 meters ahead in highway scenarios. The filtering algorithm uses a segmented clothoid-based road model. In order to use the heading of leading vehicles we need to detect if each vehicle is keeping lane or changing lane. Hence, we propose to jointly detect the motion state of the leading vehicles and estimate the road geometry using a multiple model filter. Finally the proposed algorithm is compared to an existing method using real data collected from highways. The results indicate that it provides a more accurate road estimation in some scenarios

Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations

This paper presents an algorithm for estimating the shape of the road ahead of a host vehicle equipped with the following onboard sensors: a camera, a radar, and vehicle internal sensors. The aim is to accurately describe the road geometry up to 200 m ahead in highway scenarios. This purpose is accomplished by deriving a precise clothoid-based road model for which we design a Bayesian fusion framework. Using this framework, the road geometry is estimated using sensor observations on the shape of the lane markings, the heading of leading vehicles, and the position of roadside radar reflectors. The evaluation on sensor data shows that the proposed algorithm is capable of capturing the shape of the road well, even in challenging mountainous highways