Intelligent Transportation Systems, IEEE Transactions on - new TOC

Sept. 2009

IEEE Transactions on Intelligent Transportation Systems publication information

Sept. 2009

Ahead of Traffic: Where to Next?

Sept. 2009

Guest Editorial Introducing Perception, Planning, and Navigation for Intelligent Vehicles

Sept. 2009

The nine papers in this special section examine perception, planning, and navigation for intelligent vehicles.

Stereo-Based Pedestrian Detection for Collision-Avoidance Applications

Sept. 2009

Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them. We present a new approach for standing- and walking-pedestrian detection, in urban traffic conditions, using grayscale stereo cameras mounted on board a vehicle. Our system uses pattern matching and motion for pedestrian detection. Both 2-D image intensity information and 3-D dense stereo information are used for classification. The 3-D data are used for effective pedestrian hypothesis generation, scale and depth estimation, and 2-D model selection. The scaled models are matched against the selected hypothesis using high-performance matching, based on the Chamfer distance. Kalman filtering is used to track detected pedestrians. A subsequent validation, based on the motion field's variance and periodicity of tracked walking pedestrians, is used to eliminate false positives.

Autonomous Navigation of Vehicles from a Visual Memory Using a Generic Camera Model

Sept. 2009

In this paper, we present a complete framework for autonomous vehicle navigation using a single camera and natural landmarks. When navigating in an unknown environment for the first time, usual behavior consists of memorizing some key views along the performed path to use these references as checkpoints for future navigation missions. The navigation framework for the wheeled vehicles presented in this paper is based on this assumption. During a human-guided learning step, the vehicle performs paths that are sampled and stored as a set of ordered key images, as acquired by an embedded camera. The visual paths are topologically organized, providing a visual memory of the environment. Given an image of the visual memory as a target, the vehicle navigation mission is defined as a concatenation of visual path subsets called visual routes. When autonomously running, the control guides the vehicle along the reference visual route without explicitly planning any trajectory. The control consists of a vision-based control law that is adapted to the nonholonomic constraint. Our navigation framework has been designed for a generic class of cameras (including conventional, catadioptric, and fisheye cameras). Experiments with an urban electric vehicle navigating in an outdoor environment have been carried out with a fisheye camera along a 750-m-long trajectory. Results validate our approach.

Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models

Sept. 2009

Modeling and predicting human and vehicle motion is an active research domain. Due to the difficulty of modeling the various factors that determine motion (e.g., internal state and perception), this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g., camera and laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use offline learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. In this paper, we present an approach where motion patterns can be learned incrementally and in parallel with prediction. Our work is based on a novel extension to hidden Markov models (HMMs) - called growing hidden Markov models - which gives us the ability to incrementally learn both the parameters and the structure of the model.

A Comprehensive Evaluation Framework and a Comparative Study for Human Detectors

Sept. 2009

We introduce a framework for evaluating human detectors that considers the practical application of a detector on a full image using multisize sliding-window scanning. We produce detection error tradeoff (DET) curves relating the miss detection rate and the false-alarm rate computed by deploying the detector on cropped windows and whole images, using, in the latter, either image resize or feature resize. Plots for cascade classifiers are generated based on confidence scores instead of on variation of the number of layers. To assess a method's overall performance on a given test, we use the average log miss rate (ALMR) as an aggregate performance score. To analyze the significance of the obtained results, we conduct 10-fold cross-validation experiments. We applied our evaluation framework to two state-of-the-art cascade-based detectors on the standard INRIA person dataset and a local dataset of near-infrared images. We used our evaluation framework to study the differences between the two detectors on the two datasets with different evaluation methods. Our results show the utility of our framework. They also suggest that the descriptors used to represent features and the training window size are more important in predicting the detection performance than the nature of the imaging process, and that the choice between resizing images or features can have serious consequences.

Nonlinear Constraint Network Optimization for Efficient Map Learning

Sept. 2009

Learning models of the environment is one of the fundamental tasks of mobile robots since maps are needed for a wide range of robotic applications, such as navigation and transportation tasks, service robotic applications, and several others. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we present a highly efficient maximum-likelihood approach that is able to solve 3-D and 2-D problems. Our approach addresses the so-called graph-based formulation of simultaneous localization and mapping (SLAM) and can be seen as an extension of Olson's algorithm toward non-flat environments. It applies a novel parameterization of the nodes of the graph that significantly improves the performance of the algorithm and can cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory. Furthermore, our approach is able to appropriately distribute the roll, pitch, and yaw error over a sequence of poses in 3-D mapping problems. We implemented our technique and compared it with multiple other graph-based SLAM solutions. As we demonstrate in simulated and real-world experiments, our method converges faster than the other approaches and yields accurate maps of the environment.

Real-Time Hierarchical Outdoor SLAM Based on Stereovision and GPS Fusion

Sept. 2009

This paper presents a new real-time hierarchical (topological/metric) simultaneous localization and mapping (SLAM) system. It can be applied to the robust localization of a vehicle in large-scale outdoor urban environments, improving the current vehicle navigation systems, most of which are only based on Global Positioning System (GPS). Then, it can be used on autonomous vehicle guidance with recurrent trajectories (bus journeys, theme park internal journeys, etc.). It is exclusively based on the information provided by both a low-cost, wide-angle stereo camera and a low-cost GPS. Our approach divides the whole map into local submaps identified by the so-called fingerprints (vehicle poses). In this submap level (low-level SLAM), a metric approach is carried out. There, a 3-D sequential mapping of visual natural landmarks and the vehicle location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. GPS measurements are integrated within this low-level improving vehicle positioning. A higher topological level (high-level SLAM) based on fingerprints and the multilevel relaxation (MLR) algorithm has been added to reduce the global error within the map, keeping real-time constraints. This level provides nearly consistent estimation, keeping a small degradation with GPS unavailability. Some experimental results for large-scale outdoor urban environments are presented, showing an almost constant processing time.

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Sept. 2009

Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system (McCall et al., 2007) to determine the relative usefulness of each cue for determining intent. Various combinations of input data are presented to a discriminative classifier, which is trained to output a prediction of probable lane-change maneuver at a particular point in the future. Quantitative results from a naturalistic driving study are presented and show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane-change intent prediction. The addition of eye gaze does not improve performance as much as simpler head dynamics cues. The advantage of head data over eye data is shown to be statistically significant (p < 0.01) 3 s ahead of lane-change situations, indicating that there may be a biological basis for head motion to begin earlier than eye motion during "lane-change"-related gaze shifts.

Ground-Texture-Based Localization for Intelligent Vehicles

Sept. 2009

Localization is a critical problem in the research of intelligent vehicles. Although it can be achieved by using a real-time kinematic global positioning system (RTK-GPS, or fused with other methods such as dead reckoning), it may be unfeasible if every vehicle has to be equipped with such an expensive sensor. This paper proposes a ground-texture-based map-matching approach to address the localization problem. To reduce the effect of complicated illumination in outdoor environments, a camera is fixed downward at the bottom of a vehicle, and controllable lights are also equipped around the camera for consistent illumination. The proposed approach includes two steps: 1) mapping and 2) localization. RTK-GPS is only used in the mapping, and other sensor data from camera and odometry are captured with time stamps to create a global ground texture map. A multiple-view registration-based optimization algorithm is applied to improve map accuracy. In the localization step, vehicle pose is estimated by matching the current camera frame with the best submap frame and by fusion strategy. Results with both synthetic and real experiments prove the feasibility and effectiveness of the proposed approach.

An Experimental Study on Pitch Compensation in Pedestrian-Protection Systems for Collision Avoidance and Mitigation

Sept. 2009

This paper describes an improved stereovision system for the anticipated detection of car-to-pedestrian accidents. An improvement of the previous versions of the pedestrian-detection system is achieved by compensation of the camera's pitch angle, since it results in higher accuracy in the location of the ground plane and more accurate depth measurements. The system has been mounted on two different prototype cars, and several real collision-avoidance and collision-mitigation experiments have been carried out in private circuits using actors and dummies, which represents one of the main contributions of this paper. Collision avoidance is carried out by means of deceleration strategies whenever the accident is avoidable. Likewise, collision mitigation is accomplished by triggering an active hood system.

Obstacle Detection and Tracking for the Urban Challenge

Sept. 2009

This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University 's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended by adding new sensors and validation algorithms.

A Cost-Effective Ultrasonic Sensor-Based Driver-Assistance System for Congested Traffic Conditions

Sept. 2009

In urban areas, congested traffic results in a large number of accidents at low speeds. This paper describes an accurate and fast driver-assistance system (DAS) that detects obstacles and warns the driver in advance of possible collisions in such a congested traffic environment. A laboratory prototype of the system is built and tested by simulating different weather conditions in the laboratory. The proposed DAS is also suitable as a parking-assistance system. Ultrasonic sensors are used to detect obstacles in this paper because they have several advantages over other types of sensors in short-range object detection. Multiple sensors are needed to get a full-field view because of the limited lateral detectable range of ultrasonic sensors. Furthermore, crosstalk is a common problem when multiple ultrasonic sensors are used. A simple microcontroller-based method to reduce crosstalk between sensors is described, which is achieved by firing each transducer by a pseudorandom number of pulses so that the echo of each transducer can uniquely be identified. Existing DASs need more time to reliably detect the objects, making them unsuitable for DASs, where time is a critical factor. A method to reduce the obstacle detection time of the system is also proposed. The cost of this high-performance system is expected to be very reasonable. All the practical implementation details are included. Extensive experimentation has been carried out, and the results confirm the speed and reliability of the presented system.

An Approach to Urban Traffic State Estimation by Fusing Multisource Information

Sept. 2009

This paper presents an information-fusion-based approach to the estimation of urban traffic states. The approach can fuse online data from underground loop detectors and global positioning system (GPS)-equipped probe vehicles to more accurately and completely obtain traffic state estimation than using either of them alone. In this approach, three parts of the algorithms are developed for fusion computing and the data processing of loop detectors and GPS probe vehicles. First, a fusion algorithm, which integrates the federated Kalman filter and evidence theory (ET), is proposed to prepare a robust, credible, and extensible fusion platform for the fusion of multisensor data. After that, a novel algorithm based on the traffic wave theory is employed to estimate the link mean speed using single-loop detectors buried at the end of links. With the GPS data, a series of technologies are combined with the geographic information systems for transportation (GIS-T) map to compute another link mean speed. These two speeds are taken as the inputs of the proposed fusion platform. Finally, tests on the accuracy, conflict resistance, robustness, and operation speed by real-world traffic data illustrate that the proposed approach can well be used in urban traffic applications on a large scale.

PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach

Sept. 2009

The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.

Position Control of a Wheeled Mobile Robot Including Tire Behavior

Sept. 2009

Advanced driver assistance systems are increasingly available on road vehicles. These systems require a thorough development procedure, an important part of which consists of hardware-in-the-loop experiments in a controlled environment. To this end, a facility called vehicle hardware-in-the-loop (VeHIL) is operated, aiming at testing the entire road vehicle in an artificial environment. In VeHIL, the test vehicle is placed on a roller bench, whereas other traffic participants, i.e., vehicles in the direct neighborhood of the test vehicle, are simulated using wheeled mobile robots (WMRs). To achieve a high degree of experiment reproducibility, focus is put on the design of an accurate position control system for the robots. Due to the required types of maneuvers, these robots have independently driven and steered wheels. Consequently, the robot is overactuated. Furthermore, since the robot is capable of high-dynamic maneuvers, slip effects caused by the tires can play an important role. A position controller based on feedback linearization is presented, using the so-called multicycle approach, which regards the robot as a set of identical unicycles. As a result, the WMR is position controlled, whereas each unicycle is controlled, taking weight transfer and longitudinal and lateral tire slip into account.

A Laser-Scanner-Based Approach Toward Driving Safety and Traffic Data Collection

Sept. 2009

This work is motivated by the following two potential applications: 1) enhancing driving safety and 2) collecting traffic data in a large dynamic urban environment. A laser-scanner-based approach is proposed. The problem is formulated as a simultaneous localization and mapping (SLAM) with object tracking and classification, where the focus is on managing a mixture of data from both dynamic and static objects in a highly dynamic environment. A trajectory-oriented closure is also proposed using the sporadically available global positioning system (GPS) measurements in urban areas to assist for global accuracy, particularly when the vehicle makes a noncyclical measurement in a large outdoor environment. Experiments are conducted using the data that were collected along a course near 4.5 km in a highly dynamic environment. Possibilities of the approaches toward the two potential applications are demonstrated, and avenues for future works are discussed.

Scheduling and Routing of AMOs in an Intelligent Transport System

Sept. 2009

Autonomous moving objects (AMOs), such as automated guided vehicles (AGVs) and autonomous robots, have widely been used in the industry for decades. In an intelligent transport system with a great number of AMOs involved, it is important to eliminate potential congestion and deadlocks among AMOs to maintain a well-organized traffic flow. In this paper, we propose an algorithm that adapts bitonic merge sort algorithm for concurrent scheduling and routing of a great number (i.e., 4n ²) of AMOs on an ntimesn mesh topology of path network without congestion or deadlocks among AMOs during their moves. The results are tested by experiments with randomly generated data and the comparison of a related model.

IEEE Intelligent Transportation Systems Society Information

Sept. 2009

IEEE Transactions on Intelligent Transportation Systems information for authors

Sept. 2009

Intelligent Transportation Systems, IEEE Transactions on - new TOC

Table of Contents

IEEE Transactions on Intelligent Transportation Systems publication information

Ahead of Traffic: Where to Next?

Guest Editorial Introducing Perception, Planning, and Navigation for Intelligent Vehicles

Stereo-Based Pedestrian Detection for Collision-Avoidance Applications

Autonomous Navigation of Vehicles from a Visual Memory Using a Generic Camera Model

Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models

A Comprehensive Evaluation Framework and a Comparative Study for Human Detectors

Nonlinear Constraint Network Optimization for Efficient Map Learning

Real-Time Hierarchical Outdoor SLAM Based on Stereovision and GPS Fusion

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Ground-Texture-Based Localization for Intelligent Vehicles

An Experimental Study on Pitch Compensation in Pedestrian-Protection Systems for Collision Avoidance and Mitigation

Obstacle Detection and Tracking for the Urban Challenge

A Cost-Effective Ultrasonic Sensor-Based Driver-Assistance System for Congested Traffic Conditions

An Approach to Urban Traffic State Estimation by Fusing Multisource Information

PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach

Position Control of a Wheeled Mobile Robot Including Tire Behavior

A Laser-Scanner-Based Approach Toward Driving Safety and Traffic Data Collection

Scheduling and Routing of AMOs in an Intelligent Transport System

IEEE Intelligent Transportation Systems Society Information

IEEE Transactions on Intelligent Transportation Systems information for authors