Showing papers in "IEEE Transactions on Intelligent Transportation Systems in 2002"

Detection and classification of vehicles

Abstract: This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. We also briefly describe an interactive camera calibration tool that we have developed for recovering the camera parameters using features in the image selected by the user.

Freeway ramp metering: an overview

Abstract: Recurrent and nonrecurrent congestion on freeways may be alleviated if today's "spontaneous" infrastructure utilization is replaced by an orderly, controllable operation via comprehensive application of ramp metering and freeway-to-freeway control, combined with powerful optimal control techniques. This paper first explains why ramp metering can lead to a dramatic amelioration of traffic conditions on freeways. An overview of ramp metering algorithms is provided next, ranging from early fixed-time approaches to traffic-responsive regulators and to modern sophisticated nonlinear optimal control schemes. Finally, a large-scale example demonstrates the high potential of advanced ramp metering approaches.

Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications

Abstract: Describes the technologies of cooperative driving with automated vehicles and intervehicle communications in the Demo 2000 cooperative driving. Cooperative driving, aiming at the compatibility of safety and efficiency of road traffic, means that automated vehicles drive by forming a flexible platoon over a couple of lanes with a short intervehicle distance while performing lane changing, merging, and leaving the platoon. The vehicles for the demonstration are equipped with automated lateral and longitudinal control functions with localization data by the differential global positioning system (DGPS) and the intervehicle communication function with 5.8-GHz dedicated short range communication (DSRC) designed for the dedicated use in the demonstration. In order to show the feasibility and potential of the technologies, the demonstration was held in November 2000, on a test track with five automated vehicles. The scenario included stop and go, platooning, merging, and obstacle avoidance.

Conflict resolution problems for air traffic management systems solved with mixed integer programming

Abstract: This paper considers the problem of solving conflicts arising among several aircraft that are assumed to move in a shared airspace. Aircraft can not get closer to each other than a given safety distance in order to avoid possible conflicts between different airplanes. For such system of multiple aircraft, we consider the path planning problem among given waypoints avoiding all possible conflicts. In particular we are interested in optimal paths, i.e., we want to minimize the total flight time. We propose two different formulations of the multiaircraft conflict avoidance problem as a mixed-integer linear program: in the first case only velocity changes are admissible maneuvers, in the second one only heading angle changes are allowed. Due to the linear formulation of the two problems, solutions may be obtained quickly with standard optimization software, allowing our approach to be implemented in real time.

Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET

Abstract: This paper employs previously developed modeling, validation, and stimulation tools to address, for the first time, the realistic macroscopic simulation of a real large-scale motorway network. More specifically, the macroscopic simulator METANET, involving a second-order traffic flow model as well as network-relevant extensions, is utilized. A rigorous quantitative validation procedure is applied to individual network links, and subsequently a heuristic qualitative validation procedure is employed at a network level. The large-scale motorway network around Amsterdam, The Netherlands, is considered in this investigation. The main goal of the paper is to describe the application approach and procedures and to demonstrate the accuracy and usefulness of macroscopic modeling tools for large-scale motorway networks.

Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks

Abstract: This paper extends the A* methodology to shortest path problems in dynamic networks, in which arc travel times are time dependent. We present efficient adaptations of the A* algorithm for computing fastest (minimum travel time) paths from one origin node to one destination node, for one as well as multiple departure times at the origin node, in a class of dynamic networks the link travel times of which satisfy the first-in-first-out property. We summarize useful properties of dynamic networks and develop improved lower bounds on minimum travel times. These lower bounds are exploited in designing efficient adaptations of the A* algorithm to solve instances of the one-to-one dynamic fastest path problem. The developed algorithms are implemented and their computational performance is analyzed experimentally. The performance of the computer implementations of the adaptations of the A* algorithm are compared to a dynamic adaptation of Dijkstra's algorithm, stopped when the destination node is selected. Comparative computational results obtained demonstrate that the algorithms of this paper are efficient. Using a network containing 3000 nodes, 10 000 links, and 100 time intervals, the dynamic adaptations of the A* led to a savings ratio of 11, in terms of number of nodes selected, and to a savings ratio of five in terms of computation time. The effect of the network size on the performance of these adaptations is also studied. It is shown that the computational savings in term of both the number of nodes selected and the computation time, increase with the size of the network topology.

An obstacle detection method by fusion of radar and motion stereo

Abstract: In order to avoid collision with an object that blocks the course of a vehicle, measuring the distance to it and detecting positions of its side boundaries, are necessary. In the paper, an object detection method achieved by the fusion of millimeter-wave radar and a single video camera is proposed. We consider the method as the least expensive solution because at least one camera is necessary for lane marking detection. In the method, the distance is measured by the radar, and the boundaries are found from an image sequence, based on a motion stereo technique with the help of the distance measured by the radar. Since the method does not depend on the appearance of objects, it is capable of detecting not only an automobile but also other objects. Object detection by the method was confirmed through an experiment. In the experiment, both a stationary and a moving object were detected and a pedestrian as well as a vehicle was detected.

Development of night-vision system

Abstract: A night-vision system has been developed to help reduce vehicle-pedestrian accidents occurring at night. High-temperature objects assumed to be pedestrians are detected by processing the images from infrared stereo cameras mounted on the. vehicle, and the possibility of a collision is judged by calculating the position and relative moving vectors of the pedestrian. In addition, voice guidance is provided and a highlighted infrared image of the pedestrian is displayed at the bottom of the front windshield using a head-up display (HUD). It was determined that the system could judge the possibility of collisions with pedestrians on the road or crossing the road.

Modeling and simulation for hybrid electric vehicles. II. Simulation

Abstract: For pt.I see ibid., vol.3, no.4, p.235-43 (2002). Hybrid electric vehicle (HEV) simulation is conducted based on the model developed for a parallel HEV built in the University of Tennessee, Knoxville (UT-HEV). The HEV simulation is a parametric analysis of the power control schemes and vehicle performance. Major parameters are evaluated for the vehicle driven under the standard urban and highway driving schedules. The results indicate that current configuration of the UT-HEV NEON has superb capability for battery state of charge (SOC) recovery while meeting the velocity request of the given driving schedules. Recommendations are given from the simulation for optimal performance in light of the HEV power control, capability for SOC recovery through regeneration and regenerative braking, as well as velocity, acceleration and range performance.

Heuristic techniques for accelerating hierarchical routing on road networks

Abstract: The route computation module is one of the most important functional blocks in a dynamic route guidance system. Although various algorithms exist for finding the shortest path, their performance tends to deteriorate as the network size increases. We present an efficient hierarchical routing algorithm that finds a near-optimal route and evaluate it on a large city road network. Solutions provided by the hierarchical routing algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy. We propose a novel yet simple heuristic to substantially improve the performance of the hierarchical routing algorithm with acceptable loss of accuracy. A network pruning technique has been incorporated into the algorithm to reduce the search space and the correctness of the results is evaluated. The improved hierarchical routing algorithm that incorporates the heuristic techniques has been found to be over 50 times faster than a typical shortest path algorithm.

Preceding vehicle recognition based on learning from sample images

Abstract: Preceding vehicle recognition is an important enabling technology for developing a driver assistance system and an autonomous vehicle system. However, this is difficult for computer vision to achieve because of the variety of shapes and colors in which vehicles are made. In this paper, we propose a novel vision-based preceding vehicle recognition method, which has the capability of recognizing a wide selection of vehicles. In the proposed method, classifiers learned from "vehicle" training samples and "nonvehicle" training samples are used to enable recognition. We also propose a novel classification method, the "multiclustered modified quadratic discriminant function" (MC-MQDF). The MC-MQDF is capable of estimating the complex distribution due to the variety of different possible appearances for preceding vehicles. In order to confirm the feasibility of recognizing various vehicles, and to demonstrate the advantage of the MC-MQDF over the MQDF, classification experiments were carried out using the images of various vehicles. In a complex distribution test including a variety of vehicles, the classification rate for the MC-MQDF was approximately 98%, whereas the classification rate for the ordinary MQDF technique was approximately 93%. This supports the superiority of the MC-MQDF technique over the MQDF technique, and demonstrates the feasibility of recognizing a variety of different vehicles.

Fast obstacle detection for urban traffic situations

Abstract: The early recognition of potentially harmful traffic situations is an important goal of vision-based driver assistance systems. Pedestrians, in particular children, are highly endangered in inner city traffic. Within the DaimlerChrysler urban traffic assistance (UTA) project, we are using stereo vision and motion analysis in order to manage those situations. The flow/depth constraint combines both methods in an elegant way and leads to a robust and powerful detection scheme. A ball bouncing on the road often implies a child crossing the street. Since balls appear very small in the images of our cameras and can move considerably fast, a special algorithm has been developed to achieve maximum recognition reliability.

EMS-Vision: a perceptual system for autonomous vehicles

Abstract: The paper gives a survey on the new Expectation-based Multifocal Saccadic vision (EMS-vision) system for autonomous vehicle guidance developed at the Universitat der Bundeswehr Munchen (UBM). EMS-Vision is the third generation dynamic vision system following the 4-D approach. Its core element is a new camera arrangement, mounted on a high bandwidth pan-tilt head for active gaze control. Central knowledge representation and a hierarchical system architecture allow efficient activation and control of behavioral capabilities for perception and action. The system has been implemented on commercial off-the-shelf (COTS) hardware components in both UBM test vehicles VaMoRs and VaMP Results from autonomous turnoff maneuvers, performed on army proving grounds, are discussed.

Accurate road following and reconstruction by computer vision

Abstract: This paper presents a method designed to track and to recover the three-dimensional (3-D) shape of a road by computer vision. The method is based first upon an accurate detection algorithm which provides a reliable estimation of the roadside in the image. This algorithm works by recursive updating of a statistical model of the lane obtained by an off-line training phase. Once the sides have been located, a reconstruction algorithm computes the vehicle location on its lane, the 3-D shape of the road, and gives both the sides location and their confidence interval for the next image. The detection algorithm then looks for the roadside in this interval in order to limit the computational times, which are about 30-150 ms on a HP workstation.

Quintic G/sup 2/-splines for the iterative steering of vision-based autonomous vehicles

Abstract: This paper presents a new motion planning primitive to be used for the iterative steering of vision-based autonomous vehicles. This primitive is a parameterized quintic spline, denoted as /spl eta/-spline, that allows interpolating an arbitrary sequence of points with overall second-order geometric (G/sup 2/-) continuity. Issues such as completeness, minimality, regularity, symmetry, and flexibility of these G/sup 2/-splines are addressed in the exposition. The development of the new primitive is tightly connected to the inversion control of nonholonomic car-like vehicles. The paper also exposes a supervisory strategy for iterative steering that integrates feedback vision data processing with the feedforward inversion control.

Depth-based target segmentation for intelligent vehicles: fusion of radar and binocular stereo

Abstract: Dynamic environment interpretation is of special interest for intelligent vehicle systems. It is expected to provide lane information, target depth, and the image positions of targets within given depth ranges. Typical segmentation algorithms cannot solve the problems satisfactorily, especially under the high-speed requirements of a real-time environment. Furthermore, the variation of image positions and sizes of targets creates difficulties for tracking. In this paper, we propose a sensor-fusion method that can make use of coarse target depth information to segment target locations in video images. Coarse depth ranges can be provided by radar systems or by a vision-based algorithm introduced in the paper. The new segmentation method offers more accuracy and robustness while decreasing the computational load.

Design, simulation, and evaluation of automated container terminals

Abstract: Due to the boom in world trade, port authorities are looking into ways of making existing facilities more efficient. One way to improve efficiency, increase capacity, and meet future demand is to use advanced technologies and automation in order to speed up terminal operations. In this paper, we design, analyze, and evaluate four different automated container terminal (ACT) concepts. These concepts include automated container terminals based on the use of automated guidance vehicles (AGVs), a linear motor conveyance system (LMCS), an overhead grid rail system (GR), and a high-rise automated storage and retrieval structure (AS/RS). We use future demand scenarios to design the characteristics of each terminal in terms of configuration, equipment and operations. A microscopic simulation model is developed and used to simulate each terminal system for the same operational scenario and evaluate its performance. A cost model is used to evaluate the cost associated with each terminal concept. Our results indicate that automation could improve the performance of conventional terminals substantially and at a much lower cost. Among the four concepts considered the one based on automated guidance vehicles is found to be the most effective in terms of performance and cost.

Adaptive control of a hybrid electric vehicle

Abstract: A decentralized adaptive control system (ACS) for a four motor-generator four-wheel drive hybrid electric vehicle (HEV) is designed and its ability to deal with unknown tire dynamics, changing road surfaces, and vehicle loading is evaluated. A system composed of four separate adaptive controllers is designed to control the vehicle's speed, steering, side slip, and energy management system. A nonlinear simulation model for the vehicle dynamics and its power train components is developed and used to evaluate the performance of the ACS, while the vehicle is simultaneously turning and accelerating or braking under varying loading and icing conditions.

An algorithm for distinguishing the types of objects on the road using laser radar and vision

Abstract: Describes a method for distinguishing the types of forward objects detected on and alongside the road using a vehicle-mounted scanning laser radar (SLR) and a camera. This method can measure the distance to a preceding vehicle in the same lane as well as to other forward vehicles in adjacent lanes. Objects are detected on the basis of SLR digital signal data and are categorized as vehicles, delineators, and signs based on their motion and positions relative to white lane markers. The motions of detected objects are judged by the relationship between the path of the host vehicle and changes in the positions of the objects. The host vehicle's path is computed using steering maneuver data and the vehicle velocity. White lane markers are detected by an image processing technique. The proposed algorithm has been validated in an experiment conducted with a simulator. Data recorded at a driving speed of more than 40 km/h on Japanese expressways were used in the simulation. The types of objects detected on the road were successfully distinguished as expected.

Emergency vehicle maneuvers and control laws for automated highway systems

Abstract: The operation and transit of emergency vehicles (EVs) on an automated highway system (AHS) designed under the control architecture proposed in the California PATH (Partners for Advanced Transit and Highways) program is described. The term "EVs" is used in a general sense to describe vehicles such as police cars, ambulances and tow trucks, that may service faults inside or outside the AHS. The transit of EVs requires intensive participation of the coordination layer, which directs the interactions of neighboring vehicles, and the link layer, which regulates traffic flow along stretches of highway. Various strategies for these two hierarchical layers that are needed to assign high-priority transit to EVs on the AHS are described. These coordination- and link-layer maneuvers circulate other vehicles around the EV in both free-flowing and stopped traffic.

Toward vision-based intelligent navigator: its concept and prototype

Abstract: Proposes a novel concept of an intelligent navigator that can give a driver timely advice on safe and efficient driving. From both the current traffic conditions obtained from visual data and the driver's goals and preferences in driving, it autonomously generates advice and gives it to the driver. Not only can operational-level advice be generated, such as emergency braking due to an abrupt deceleration of the vehicle in front, but also tactical-level advice, such as lane changing due to a congested situation ahead. Two main components of the intelligent navigator - the advice generation system and the road scene recognition system - are explained. A three-level reasoning architecture is proposed for generating advice in dynamic and uncertain traffic environments. Online experiments using the prototype system show the potential feasibility of the proposed concept.

A hierarchical view on modeling the reliability of a DSRC link for ETC applications

Abstract: For electronic payments, the communication link has to be reliable. Dedicated short-range communication (DSRC) is a proposed solution for the automatic debiting of vehicles in an electronic toll collection (ETC) system without disturbing the traffic flow. The requirements on the reliability of such a system are high, which implies that only large-scale simulations with a lot of detail are effective for analyzing occasional errors. In this paper, a hierarchical approach is worked out that allows such simulations of the communication link, with a 80% reduction of the computational effort compared to simulation with full detail.

A robust word boundary detection algorithm for variable noise-level environment in cars

Abstract: This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise in cars. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed and sets fixed thresholds to find the boundary of word signal. In fact, the background noise level in cars varies in the procedure of recording due to speed change and moving environment, and some thresholds should be tuned according to the variation of background noise level. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. To solve this problem, we propose a minimum mel-scale frequency band (MiMSB) parameter which can estimate the varying background noise level in cars by adaptively choosing one band with minimum energy from the mel-scale frequency bank. With the MiMSB parameter, some preset thresholds used to find the boundary of word signal are no longer fixed in all the recording intervals. These thresholds will be tuned according to the MiMSB parameter. We also propose an enhanced time frequency (ETF) parameter by extending the time-frequency (TF) parameter proposed by Junqua et al. from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise. The ETF parameter can extract useful frequency information by choosing some bands of the mel-scale frequency bank. Based on the MiMSB and ETF parameters, we finally propose a new robust algorithm for word boundary detection in variable noise-level environment. The new algorithm has been tested over a variety of noise conditions in cars and has been found to perform well not only under variable background noise level condition, but also under fixed background noise level condition. The new robust algorithm using the MiMSB and ETF parameters achieved higher recognition rate than the TF-based robust algorithm, which has been shown to outperform several commonly used algorithms, by about 5% in variable background noise level condition. It also reduced the recognition error rate due to endpoint detection to 25%, compared to an average of 34% obtained with the TF-based robust algorithm.

Artificial vision in extreme environments for snowcat tracks detection

Abstract: Describes the image processing techniques designed to localize the tracks of snowcats for the automation of transportation of goods and people during the Italian scientific missions in Antarctica. The final goal is to enable a snowcat to automatically follow the preceding one in a train-like fashion. A camera is used to acquire images of the scene; the image sequence is analyzed by a computer vision system which identifies the tracks and produces a high level description of the scene. This result is then forwarded to a further software module in charge of the control of the snowcat movement. A further optional representation, in which markers highlighting the tracks are superimposed onto the acquired image, is transmitted to a human supervisor located off board. This system has been tested in the Italian test site and was under testing in the South Pole during the early 2002 Italian scientific mission. The paper also briefly describes an alternative solution based on an evolutionary approach.

A self-describing data transfer model for ITS applications

Abstract: The wide variety of remote sensors used in Intelligent Transportation Systems (ITS) applications (loops, probe vehicles, radar, cameras, etc.) has created a need for general methods by which data can be shared among agencies and users who own disparate computer systems. In this paper, we present a methodology that demonstrates that it is possible to create, encode, and decode a self-describing data stream using: 1) existing data description language standards; 2) parsers to enforce language compliance; 3) a simple content language that flows out of the data description language; and 4) architecture neutral encoders and decoders based on ASN.1.

Feasibility study on a highly mobile microwave-band broad-band telecommunication system

Abstract: A prototype of a wireless transmission system has been developed that can transmit multimedia-information data at a maximum rate of 4.608 Mb/s in the 5.2-GHz carrier frequency band. It is based on a multicode transmission scheme that uses cyclic shifted-and-extended M-sequences and is designed for applications such as intelligent transport systems (ITS) and future broadband mobile communication systems. The proposed scheme is highly robust against high-speed multipath fading. The transmission performance of the prototype system was measured in field trials at an outdoor ITS experimental course. The real-time data-transmission rate between the base and mobile stations was 4.608 Mb/s at a vehicle speed of 80 km/h.

Investigation of calculation/distance measurement method using spread-spectrum communications system

Abstract: Recently, extensive intelligent transport systems (ITS) studies using spread-spectrum communications have got underway. Such studies have suggested a calculation/distance (c/d) measurement method for securing passengers more efficiently in public transportation systems and the like. This method maintains communication between a base station and a small device, like a card, carried by the passenger. This makes it possible to perform calculations and measurements in real-time. The cards send an individual information in each c/d card (ID information) in response to the base station. The base station calculates the correlation value by using spread-spectrum signals, then performs the calculations and distance measurements. As the result of a simulation, good results were obtained in this study.