Intelligent and automatic systems are making our daily life easier. They are able
to automate tasks that, up to now, were performed by humans, freeing them from these
tedious tasks. They are mainly based on the classical robotic architectures where
the stages of perception—using different sensor sources or even a fusion of a set
of them—and planning—where intelligent control systems are applied—play a key role.
Among all of the fields in which intelligent systems can be applied, transport systems
are considered one of the most promising ones since over one million fatalities—including
drivers, pedestrians, cyclists and motorcyclists—are registered each year worldwide
and they can definitively help to reduce these figures.
The growth in the number of drivers in the last decades, and consequently in the number
of vehicles, has made traffic accidents become a major concern in the road transportation
sector. Solutions such as building new transport infrastructures—specifically in urban
areas where space limitations make it almost impossible—or increasing fines for inappropriate
driving have not achieved the goal of reducing the number of fatalities, either in
urban environments or highways. New policies on the part of the Governments are focused
on the development of a safer, more reliable and more efficient transportation supported
by intelligent systems.
The field of research in which all these topics fall into is well-known as intelligent
transportation systems (ITS). ITS focuses on improving road safety by acting on two
possible aspects: on the one hand, the infrastructure and the possibilities of optimizing
traffic flow, and on the other hand, acting on the cars with the development of Advanced
Driver Assistance Systems (ADAS) and the possibilities of optimizing traffic safety.
The final goal is trying to reduce the number of collisions, or at least to mitigate
their consequences. In this connection, one of the currently hot topics in the road
transportation field is the development of intelligent devices and systems for improving
both traffic flow and safety. New advances in positioning, navigation or perception
systems can clearly contribute to these goals. Additionally, these systems can also
be adapted for the development of service robots in an effort to increase the quality
of life for metropolitan area citizens.
Bearing the goal of helping people in their daily life in mind, sensors are critical
since they can be used to detect potential risk situations in advance. In the automotive
industry, size and cost are two fundamental limitations when it comes to introduce
new ADAS. Recently, intelligent functionalities have been included in mass-produced
vehicles, such as automatic parking systems—mainly based on ultrasonic sensors—where
steering wheel, brake and throttle pedal are automatically managed to park the car
without driver intervention; blind spot detection—mainly based on vision sensors—where
a visual or audible signal indicates a potential risk situation for lane-change maneuvers
because a vehicle is driving pretty close in an adjacent lane; or the adaptive cruise
control system—mainly based on lidar/radar technology—where longitudinal vehicle control
is automatically regulated to adapt its speed to the preceding car in a safe and reliable
manner.
In spite of the significant contributions of all of these advances, there is still
a long way to go. More sophisticated systems will have to be developed towards the
up-to-now utopia of driverless cars driving in real traffic conditions. To this end,
the combination of intelligent autonomous systems capable of analyzing all traffic
conditions and sensors for detecting all potential situations in which vehicles are
involved, will definitively contribute to improve road transportation.
As it was previously stated, perception is the first stage in the classical robotic
control architecture and, consequently, the initial critical task when it comes to
manage robot behavior because it gives information about its environment and the robot
itself. Advances in this field will lead to the development of faster, smaller, more
autonomous and more accurate robots. Concerning to this topic, this Special Issue
presents some perception systems based on the fusion of different sensors such as:
monocular and stereo vision, laser, sonar, GPS, inertial sensors, radar, etc., applied
to the localization, navigation, mapping and interaction of different prototypes.
Important sensorial research for indoor ground, all-terrain rovers, humanoids, underwater
and aerial robots, or even for assisting the humans, is being presented in the papers.
For the planning stage in the robotic control architecture, a proper management of
the received information coming from the perception phase—i.e., sensorial systems—has
to be done. Control systems use the most valuable information from the perception
stage in order to develop intelligent behaviors for the robotic systems. Concerning
to this topic, this Special Issue presents some automatic control systems based on
robust control theory, artificial intelligent techniques or advanced filtering systems
for obtaining a good robot behavior or considerably improving its position and navigation
strategies. The significant contribution of experimental papers for the Special Issue
where the systems are not only tested in simulations environments but also in real
experimental platforms where more challenging and unexpected situations occur is also
noteworthy.
This Special Issue traces in part its origin to two workshops—the Workshop on Perception
in Robotics and the Workshop on Navigation, Perception, Accurate Positioning and Mapping
for Intelligent Vehicles—organized during the IEEE Intelligent Vehicles Symposium
held in June 2012 in Alcalá de Henares (Spain). The workshop proceedings were successful,
and a Special Section of the Open Access Sensors journal dealing with the same theme
was planned. A call for papers was issued. The received papers accepted after a rigorous
review and revision cycle are included in this Special Section. The thirty three papers
that appear in this Special Issue cover the full range of new trends in perception
and autonomous control applied to Intelligent Transportation Systems, specially focused
in Intelligent Vehicles and Robotics. These papers are summarized as follows.
1.
Papers in the Special Issue
Papers presented in this Special Issue can be divided in three main groups according
to the device where the control or the perception systems is applied: humans, robots
and intelligent vehicles.
1.1.
Contributions Related to Humans
In [1] an algorithm for estimating a pedestrian location in an urban environment is
presented. The algorithm is based on the particle filter and uses different data sources:
a GPS receiver, inertial sensors, probability maps and a stereo camera. Authors claim
that the algorithm is able to estimate a pedestrian's location with an error smaller
than 2 m 90% of the time.
The aim of [2] is focused on the design of an obstacle detection system for assisting
visually impaired people. A stereo camera carried by the user is employed as sensor
and a dense disparity map is computed from the images. The system is completed with
acoustic feedback. The system is validated by means of real tests using four volunteers.
1.2.
Contributions Related to Robots
In [3] a matrix Kalman filter (MKF) using as inputs several on-board sensors has been
implemented for an integrated indoor navigation system based on a 4 wheels robotic
platform. The MKF rearranges the original nonlinear process model in a pseudo-linear
process model. They employ the Lie derivatives criterion about observability rank
to verify the conditions under which the nonlinear system is observable.
A modified-FastSLAM algorithm is proposed in [4] for the navigation of an open-frame
autonomous underwater vehicle (AUV) using as active sensor a mechanical scanning imaging
sonar. Experimental results show the authors' proposal is much more accurate and effective
compared with the methods of the current state-of-the-art.
In [5] the problem of autonomous exploration of unknown environments with single and
multiple robots is analyzed. Its authors propose an approach based on the combination
of a behavior-based navigation and an efficient data structure where previously visited
regions are stored. Experiments performed using a robotic simulator and a real platform
show a good behavior from the proposed system.
In [6] the authors present an adaptive fusion method to improve the accuracy and reliability
of the altitude measure for small Unmanned Aerial Rotorcraft (UAR) during the landing
process. The effectiveness of the proposed method is proved by static tests, autonomous
landing and hovering flight tests.
In [7] an outdoors laser-based pedestrian tracking system using a set of mobile robots
is presented, where each of them operates close to the others. Each robot detects
pedestrians from its own laser scan image, tracks them and broadcasts the tracking
information to multiple robots. Using this cooperative strategy, all the robots have
the information any of them detects. The simulation and experimental results show
that this proposal works better than conventional individual tracking does.
The work in [8] shows a 3D terrain reconstruction technique from a mobile robot. Authors
propose a new ground segmentation method for a voxel map. Authors show the time required
for ground segmentation is faster than for data sensing and it can be applied in real-time.
Work developed in [9] outlines a cross-coupled controller for a 4-wheel-robot, which
optimizes the wheel motors' control algorithm to reduce slip effects that could appear
with conventional controllers. Experimental results were carried out using an all-terrain
rover working in an agricultural terrain. They show the effectiveness of the proposal
when it comes to reduce slippage and posture errors in the vehicle.
In [10] a new scheme for Doppler Velocity Log (DVL) aided Strapdown Inertial Navigation
Systems (SINS) alignment using Unscented Kalman Filter (UKF) for AUV is presented,
which allows large initial misalignments. Experimental results show that the proposed
DVL-aided alignment model is works independently of any initial heading errors.
The work in [11] presents a system composed by a ground and an aerial robot that cooperate
sharing sensor information. The terrestrial robot is able to navigate in an unknown
large environment aided by visual feedback from the aerial robot through an on board
camera. The proposal shows outstanding results in SLAM applications in large outdoor
environments.
Authors in [12] implement a dynamic visual memory to store the information gathered
from a moving camera on board a robot. An attention system to choose where to look
with this camera and a visual localization algorithm based on this visual memory complete
the authors' proposal. Experimental results, both with simulated and real Pioneer
and Nao robots, validate the proposal in office scenarios.
The work developed in [13] implements a low-cost tele-operated system in order to
replicate the movements in a small humanoid robot. A feedback stability control and
a fuzzy control based on low-cost open platforms have been developed. Some experimental
results validate the system.
The goal of [14] is to solve the problem of dynamic obstacle avoidance for a mobile
platform by using the stochastic optimal control in terms of safety and energy efficiency
framework to compute paths. Authors propose a 3D extension of the Bayesian Occupancy
Filter (BOF) to deal with the noise in the sensor data. Some experimental results
highlight the advantages of the proposal against classical algorithms.
The work in [15] is concerned with the use of a mobile ground-based panoramic radar
sensor. This is able to deliver both distance and velocity of multiple targets in
its surrounding. The authors' purpose is to study data distortion and Doppler effect
in order to estimate vehicle's movements. Some radar-only localization and mapping
experimental results are presented for a ground-vehicle moving at high speed.
1.3.
Contributions Related to Intelligent Vehicles
In [16] a dedicated public urban transportation service access system named Mobi+
has been introduced. This facilitates the mobility of disabled, wheelchair and blind
passengers. So far, the Mobi+ system has been implemented on the buses and stations
of line ‘2’ in the city of Clermont-Ferrand (France). The experimental results show
that Mobi+ provides an effective urban bus access service for people with disabilities
and it is easily to deploy in the buses and at bus stations.
In [17] a real time speed supervisor based on road sign recognition able to work in
urban and non-urban environments is presented. The system can recognize 135 road signs
and sends warning messages to the driver. The advantages and disadvantages of the
two main methods traditionally used for detection and recognition of road signs [template
matching (TM) and neural networks (NN)] are shown.
The authors of [18] describe the perception system designed for the intelligent vehicle
that won the 2010 Future Challenge (SmartV-II). This system uses the fusion of multiple
lasers and cameras to realize several functions of autonomous navigation (road curb
detection, lane detection and traffic sign recognition). The experimental results
validate the proposed system.
A reliable freestanding position-based routing algorithm (FPBR) for highway scenarios
in the context of Vehicular Ad Hoc Networks (VANETs) is proposed in [19]. FPBR performance
is compared to one of the leading protocols for highway scenarios showing that FPBR
yields similar results, when considering free space propagation conditions, and outperforms
the leading protocol when considering a realistic highway path loss model.
The work in [20] presents a two-layer based enhanced map for supporting navigation
in urban environments. One layer is dedicated to describe the drivable road focusing
on the accurate description of its bounds. The other layer depicts building heights
and locations enabling the detection of non-line-of-sight signals coming from GPS
in an indirect view. The methodology for creating these enhanced maps is shown in
the paper.
The study in [21] presents the development of an intelligent parking service called
iParking. With this service users, parking facilities and service providers are connected
through Internet. The client software is an application running on a smartphone based
on a precise indoor positioning solution. Experimental results show the iParking working
in a real parking environment at a shopping mall.
In [22] authors describe a framework to combine experts' judgments for the prevention
of driving risks in a cabin truck simulator. Three experts were asked to evaluate
the driving risk using a Visual Analog Scale. Numerical results show that the proposal
is suitable for embedding in real-time systems.
The paper in [23] presents a vehicle dynamics prediction system consisting of a sensor
fusion system and a vehicle identification one. Comparing with most important works
in this field, the proposal improves the prediction accuracy both by incorporating
more vehicle dynamics to the prediction and by minimizing the vehicle modeling errors.
The work in [24] presents an integrated approach to examine the force exertion and
movement pattern during continuous steering movement (CSM) of the upper extremity
(UE). These findings help us to understand CSM and have important implications for
practice in clinical training.
In [25] the authors present a forward collision warning system, based on a laser scanner,
which is able to detect several potential danger situations. Decision algorithms determine
the most convenient manoeuvre, act on the actuators of the ego-vehicle and transmit
this information to other vehicles using V2V communications. The system has been tested
for overtaking manoeuvres under different real scenarios.
Authors in [26] detail an advanced GNSS/IMU fusion system based on a context-aided
Unscented Kalman filter for navigation of Intelligent Vehicles in urban conditions.
An exhaustive analysis has been carried out with available data to show the effectiveness
of the proposal.
The work in [27] proposes a method for monitoring driver safety levels using a data
fusion approach and developed in the form of an application for an Android-based Smartphone
device. Experimental results in simulation show the benefits of the fusion in providing
a more effective driver safety monitoring.
Using a 3D urban model to forecast satellite visibility in urban contexts to improve
GPS positioning is the main topic of the paper presented in [28]. The authors propose
a virtual image processing that detects and eliminates possible faulty measurements.
This closed-loop real-time proposal has shown very promising test results.
In [29] authors test a system that uses low-cost sensors, based on MEMS technology,
coupled with information derived from a video camera on-board a two-wheel motor vehicle.
They present a method for the reconstruction of the trajectory of a “Vespa” scooter
as alternative to the well-know approach based on GPS/INS fusion.
In [30] an autonomous docking system for electric vehicles recharging is presented.
It is based on an infrared camera installed in the infrastructure. A visual servoing
system coupled with an automatic controller allows the vehicle to dock to the recharging
booth in a street parking area. The prototype has shown good behavior in the city
of Paris.
The paper in [31] proposes an approach for lane segmentation and tracking that is
robust to varying shadows and occlusions. The results show that the proposed approach
performs better than the traditional gradient-based approach under the difficulties
caused by shadows and occlusions.
In [32] authors propose a technique that involves optical flow and driver's kinematics
analysis to improve the robustness of the driver's alert state under pose changes
using a single camera with NIR illumination. Author show the effectiveness of the
approach in an experiment involving 15 persons in a simulator under different levels
of sleep deprivation.
The ADAS proposed in [33] aims to prevent unsafe steering commands by means of a haptic
handwheel. The paper addresses system requirements and provides implementation details
to tele-operate two different off/on-axle combinations of a tracked mobile robot pulling
and pushing two different trailers.