Rubén Izquierdo

Road curb and lanes detection for autonomous driving on urban scenarios

This paper addresses a framework for road curb and lanes detection in the context of urban autonomous driving, with particular emphasis on unmarked roads. Based on a 3D point cloud, the 3D parameters of several curb models are computed using curvature features and Conditional Random Fields (CRF). Information regarding obstacles is also computed based on the 3D point cloud, including vehicles and urban elements such as lampposts, fences, walls, etc. In addition, a gray-scale image provides the input for computing lane markings whenever they are present and visible in the scene. A high level decision-making system yields accurate information regarding the number and location of drivable lanes, based on curbs, lane markings, and obstacles. Our algorithm can deal with curbs of different curvature and heights, from as low as 3 cm, in a range up to 20 m. The system has been successfully tested on images from the KITTI data-set in real traffic conditions, containing different number of lanes, marked and unmarked roads, as well as curbs of quite different height. Although preliminary results are promising, further research is needed in order to deal with intersection scenes where no curbs are present and lane markings are absent or misleading.

Stereo-based pedestrian detection in crosswalks for pedestrian behavioural modelling assessment

In this paper, a stereo- and infrastructure-based pedestrian detection system is presented to deal with infrastructure-based pedestrian safety measurements as well as to assess pedestrian behaviour modelling methods. Pedestrian detection is performed by region growing over temporal 3D density maps, which are obtained by means of stereo reconstruction and background modelling. 3D tracking allows to correlate the pedestrian position with the different pedestrian crossing regions (waiting and crossing areas). As an example of an infrastructure safety system, a blinking luminous traffic sign is switched on to warn the drivers about the presence of pedestrians in the waiting and the crossing regions. The detection system provides accurate results even for nighttime conditions: an overall detection rate of 97.43% with one false alarm per each 10 minutes. In addition, the proposed approach is validated for being used in pedestrian behaviour modelling, applying logistic regression to model the probability of a pedestrian to cross or wait. Some of the predictor variables are automatically obtained by using the pedestrian detection system. Other variables are still needed to be labelled using manual supervision. A sequential feature selection method showed that time-to-collision and pedestrian waiting time (both variables automatically collected) are the most significant parameters when predicting the pedestrian intent. An overall predictive accuracy of 93.10% is obtained, which clearly validates the proposed methodology.

Assistive Pedestrian Crossings by Means of Stereo Localization and RFID Anonymous Disability Identification

Assistive technology usually refers to systems used to increase, maintain, or improve functional capabilities of individuals with disabilities. This idea is here extended to transportation infrastructures, using pedestrian crossings as a specific case study. We define an Assistive Pedestrian Crossing as a pedestrian crossing able to interact with users with disabilities and provide an adaptive response to increase, maintain or improve their functional capabilities while crossing. Thus, the infrastructure should be able to locate the pedestrians with special needs as well as to identify their specific disability. In this paper, user location is obtained by means of a stereo-based pedestrian detection system. Disability identification is proposed by means of a RFID-based anonymous procedure from which pedestrians are only required to wear a portable and passive RFID tag. Global nearest neighbor is applied to solve data association between stereo targets and RFID measurements. The proposed assistive technology is validated in a real crosswalk, including different complex scenarios with multiple RFID tags.

A Comparative Analysis of Decision Trees Based Classifiers for Road Detection in Urban Environments

In this paper a comparative analysis of decision trees based classifiers is presented. Two different approaches are presented, the first one is a speficic classifier depending on the type of scene. The second one is a general classifier for every type of scene. Both approaches are trained with a set of features that enclose texture, color, shadows, vegetation and other 2D features. As well as 2D features, 3D features are taken into account, such as normals, curvatures and heights with respect to the ground plane. Several tests are made on five different classifiers to get the best parameters configuration and obtain the importance of each features in the final classification. In order to compare the results of this paper with the state of the art, the system has been tested on the KITTI Benchmark public dataset.

Analysis of ITS-G5A V2X communications performance in autonomous cooperative driving experiments

In this paper the performance of ITS-G5A communications for an autonomous driving application is analyzed in a real high-density scenario. The data was collected during the cooperative platooning tests that took place in Helmond in the frame of the Grand Cooperative Driving Challenge 2016. In the competition, between 8–10 autonomous vehicles formed two platoons in different lanes and were required to merge into a predefined competition zone. The performance is characterized using CAM CCDFs which serves as a base for the evaluation of a Cooperative Adaptive Cruise Control application. Two important effects has been identified that affect to the reliability of the communications. Firstly, there is a degradation with the distance that appears to be stronger for cars and more gentle for trucks. This indicates that occlusions heavily affect the connectivity of ITS-G5A. Secondly, the reliability is below expectations and some of the vehicles perform consistently worse than others. Although further investigation is required, a possible explanation for this is that a highly congested channel is making some of the vehicles get stuck and are not able to regularly access the channel.

Comparison between UHF RFID and BLE for Stereo-Based Tag Association in Outdoor Scenarios

Stereo-based object detection systems can be greatly enhanced thanks to the use of wireless identification technology. By combining tag localization with its identification capability, new features can be associated with each detected object, extending the set of potential applications. The main problem consists in the association between wireless tags and objects due to the intrinsic limitations of Received Signal Strength Indicator-based localization approaches. In this paper, an experimental comparison between two specific technologies is presented: passive UHF Radio Frequency IDentification (RFID) and Bluetooth Low Energy (BLE). An automatic calibration process is used to model the relationship between RSSI and distance values. A robust data association method is presented to deal with complex outdoor scenarios in medium sized areas with a measurement range up to 15m. The proposed approach is validated in crosswalks with pedestrians wearing portable RFID passive tags and active BLE beacons.

Two-camera based accurate vehicle speed measurement using average speed at a fixed point

In this paper we present a novel two-camera-based accurate vehicle speed detection system. Two high-resolution cameras, with high-speed and narrow field of view, are mounted on a fixed pole. Using different focal lengths and orientations, each camera points to a different stretch of the road. Unlike standard average speed cameras, where the cameras are separated by several kilometers and the errors in measurement of distance can be in the order of several meters, our approach deals with a short stretch of a few meters, which involves a challenging scenario where distance estimation errors should be in the order of centimeters. The relative distance of the vehicles w.r.t. the cameras is computed using the license plate as a known reference. We demonstrate that there is a specific geometry between the cameras that minimizes the speed error. The system was tested on a real scenario using a vehicle equipped with DGPS to compute ground truth speed values. The obtained results validate the proposal with maximum speed errors <; 3kmh at speeds up to 80kmh.

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems.

Fusing directional passive UHF RFID and stereo vision for tag association in outdoor scenarios

Stereo-based object detection systems can be greatly enhanced thanks to the use of passive UHF RFID technology. By combining tag localization with its identification capability, new features can be associated with each detected object, extending the set of potential applications. The main problem consists in the association between RFID tags and objects due to the intrinsic limitations of RSSI-based localization approaches. In this paper, a new directional RSSI-distance model is proposed taking into account the angle between the object and the antenna. The parameters of the model are automatically obtained by means of a stereo-RSSI automatic calibration process. A robust data association method is presented to deal with complex outdoor scenarios in medium sized areas with a measurement range up to 15m. The proposed approach is validated in crosswalks with pedestrians wearing portable RFID passive tags.