David Fernandez-Llorca

Autonomous Navigation and Obstacle Avoidance of a Micro-bus

At present, the topic of automated vehicles is one of the most promising research areas in the field of Intelligent Transportation Systems (ITS). The use of automated vehicles for public transportation also contributes to reductions in congestion levels and to improvements in traffic flow. Moreover, electrical public autonomous vehicles are environmentally friendly, provide better air quality and contribute to energy conservation. The driverless public transportation systems, which are at present operating in some airports and train stations, are restricted to dedicated roads and exhibit serious trouble dynamically avoiding obstacles in the trajectory. In this paper, an electric autonomous mini-bus is presented. All datasets used in this article were collected during the experiments carried out in the demonstration event of the 2012 IEEE Intelligent Vehicles Symposium that took place in Alcalá de Henares (Spain). The demonstration consisted of a route 725 metres long containing a list of latitude-longitude points (waypoints). The mini-bus was capable of driving autonomously from one waypoint to another using a GPS sensor. Furthermore, the vehicle is provided with a multi-beam Laser Imaging Detection and Ranging (LIDAR) sensor for surrounding reconstruction and obstacle detection. When an obstacle is detected in the planned path, the planned route is modified in order to avoid the obstacle and continue its way to the end of the mission. On the demonstration day, a total of 196 attendees had the opportunity to get a ride on the vehicles. A total of 28 laps were successfully completed in full autonomous mode in a private circuit located in the National Institute for Aerospace Research (INTA), Spain. In other words, the system completed 20.3 km of driverless navigation and obstacle avoidance.

Assistive Intelligent Transportation Systems: The Need for User Localization and Anonymous Disability Identification

The main goal of Assistive Technology (AT) is to ensure the functional independence of disabled individuals. This paper proposes the definition of a new concept of AT within the context of the ITS, Assistive Intelligent Transportation System (AITS), analyzing its intrinsic requirements and providing a set of examples. We demonstrate that AITS must localize users with disabilities and identify their specific type of impairment in order to provide an efficient response, and we propose a specific procedure to guarantee anonymity while identifying the type of disability. Moreover, this new type of AT is illustrated by means of a new assistive intelligent pedestrian crossing application that is capable of localizing pedestrians with disabilities, identifying the specific type of impairment and providing an adaptive response to enhance functional capabilities of impaired pedestrians while crossing. By combining stereo-based object detection with radio-frequency identification technology (RFID and Bluetooth Low Energy), a specific solution to the problem of user localization and anonymous disability identification is proposed. Our approach has been validated in a real crosswalk scenario and it may be extended to other types of AITS, depending on the localization accuracy requirements and the range of operation of the specific application.

Parking Assistance System for Leaving Perpendicular Parking Lots: Experiments in Daytime\/Nighttime Conditions

Backing-out and heading-out maneuvers in perpendicular or angle parking lots are one of the most dangerous maneuvers, especially in cases where side parked cars block the driver view of the potential traffic flow. In this paper, a new vision-based Advanced Driver Assistance System (ADAS) is proposed to automatically warn the driver in such scenarios. A monocular grayscale camera was installed at the back-right side of a vehicle. A Finite State Machine (FSM) defined according to three CAN Bus variables and a manual signal provided by the user is used to handle the activation/deactivation of the detection module. The proposed oncoming traffic detection module computes spatio-temporal images from a set of predefined scan-lines which are related to the position of the road. A novel spatio-temporal motion descriptor is proposed (STHOL) accounting for the number of lines, their orientation and length of the spatio-temporal images. Some parameters of the proposed descriptor are adapted for nighttime conditions. A Bayesian framework is then used to trigger the warning signal using multivariate normal density functions. Experiments are conducted on image data captured from a vehicle parked at different location of an urban environment, including both daytime and nighttime lighting conditions. We demonstrate that the proposed approach provides robust results maintaining processing rates close to real time.

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.

A Hybrid Vision-Map Method for Urban Road Detection

A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

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.

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.