Road Friction Estimation for Connected Vehicles using Supervised Machine Learning

In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both historical friction data from the connected cars and data from weather stations, and comparative results from different methods are presented. The problem is formulated as a classification task where the available data is used to train three machine learning models including logistic regression, support vector machine, and neural networks to predict the friction class (slippery or non-slippery) in the future for specific road segments. In addition to the friction values, which are measured by moving vehicles, additional parameters such as humidity, temperature, and rainfall are used to obtain a set of descriptive feature vectors as input to the classification methods. The proposed prediction models are evaluated for different prediction horizons (0 to 120 minutes in the future) where the evaluation shows that the neural networks method leads to more stable results in different conditions.Comment: Published at IV 201

Adaptive obstacle detection for mobile robots in urban environments using downward-looking 2D LiDAR

Environment perception is important for collision-free motion planning of outdoor mobile robots. This paper presents an adaptive obstacle detection method for outdoor mobile robots using a single downward-looking LiDAR sensor. The method begins by extracting line segments from the raw sensor data, and then estimates the height and the vector of the scanned road surface at each moment. Subsequently, the segments are divided into either road ground or obstacles based on the average height of each line segment and the deviation between the line segment and the road vector estimated from the previous measurements. A series of experiments have been conducted in several scenarios, including normal scenes and complex scenes. The experimental results show that the proposed approach can accurately detect obstacles on roads and could effectively deal with the different heights of obstacles in urban road environments

RoADS: A road pavement monitoring system for anomaly detection using smart phones

Monitoring the road pavement is a challenging task. Authorities spend time and finances to monitor the state and quality of the road pavement. This paper investigate road surface monitoring with smartphones equipped with GPS and inertial sensors: accelerometer and gyroscope. In this study we describe the conducted experiments with data from the time domain, frequency domain and wavelet transformation, and a method to reduce the effects of speed, slopes and drifts from sensor signals. A new audiovisual data labelling technique is proposed. Our system named RoADS, implements wavelet decomposition analysis for signal processing of inertial sensor signals and Support Vector Machine (SVM) for anomaly detection and classification. Using these methods we are able to build a real time multiclass road anomaly detector. We obtained a consistent accuracy of ≈90% on detecting severe anomalies regardless of vehicle type and road location. Local road authorities and communities can benefit from this system to evaluate the state of their road network pavement in real time

The Extraction of a Road Centre Line from Airborne Laser Scanning Data

Due to its speed and accuracy the Global Positioning System (GPS) is widely used as a data collection tool. Problems however can occur when this GPS data is used in conjunction with existing National Mapping Agencies (NMA) vector databases that are not of comparable accuracy. Shifts and misalignments of the datasets can occur. In talks with the Irish mapping agency, Ordnance Survey Ireland (OSi), prior to this project, it viewed with interest the possibility of using Airborne Laser Scanning (ALS) data as a general quality indicator of existing vector databases. The aim of this research was to extract the centre line of a small segment of straight road from triangulated ALS ground points. ALS data with a point density of 2 points per square metre was processed using TerraScan to yield a set of ground points. The extraction process was based on the creation and analysis of cross-sections taken at regular intervals from the triangulated ALS data. The cross-section widths and intervals were based on a search template developed from the start and end coordinates of an assumed centre line taken from an existing vector database. The cross-sections developed were based on individual triangles of the triangulation, groups of triangles and on interpolated data. Parameters of gradient, intensity and interpolated height are investigated. Algorithms were developed in MatLab to create and semi-automatically analyse the cross-sections. Cross-sections were generated for two different road sections and a ground truth survey was conducted for one of the roads. The most useful cross-sections were those based on Interpolated Heights from the triangulated ALS data using the road width as an additional parameter. Results demonstrated that it was possible to define the true road extent from the ALS data with accuracy equal to its point density of 2m by using a linear Least Squares best-fit algorithm. The Intensity of the return pulse was not used in the extraction process and formed a separate piece of research. The findings were that the most useful cross-section were those based on the Intensity Standard Deviations of the vertices of individual triangles in the triangulated ALS data and on Interpolated Intensity. Results show that it is possible to detect road markings from this information