Showing papers in "IEEE Transactions on Intelligent Transportation Systems in 2006"
TL;DR: The authors study the impacts of CACC for a highway-merging scenario from four to three lanes and show an improvement of traffic-flow stability and a slight increase in Trafficflow efficiency compared with the merging scenario without equipped vehicles.
Abstract: Cooperative adaptive cruise control (CACC) is an extension of ACC. In addition to measuring the distance to a predecessor, a vehicle can also exchange information with a predecessor by wireless communication. This enables a vehicle to follow its predecessor at a closer distance under tighter control. This paper focuses on the impact of CACC on traffic-flow characteristics. It uses the traffic-flow simulation model MIXIC that was specially designed to study the impact of intelligent vehicles on traffic flow. The authors study the impacts of CACC for a highway-merging scenario from four to three lanes. The results show an improvement of traffic-flow stability and a slight increase in traffic-flow efficiency compared with the merging scenario without equipped vehicles
1,347 citations
TL;DR: A comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented.
Abstract: Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented
1,056 citations
TL;DR: A non-intrusive prototype computer vision system for real-time monitoring driver's vigilance based on a hardware system, for real time acquisition of driver's images using an active IR illuminator, and their software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance.
Abstract: This paper presents a nonintrusive prototype computer vision system for monitoring a driver's vigilance in real time. It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. Some experimental results and conclusions about the performance of the system are presented
754 citations
TL;DR: A review in the related literature presented in this paper reveals that better performance has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low.
Abstract: In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low
740 citations
TL;DR: Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data.
Abstract: A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data
652 citations
TL;DR: Experimental results show that the proposed automatic traffic surveillance system is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
Abstract: This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
458 citations
TL;DR: Simulation results show that the hybridmultiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases.
Abstract: Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner
334 citations
TL;DR: The experimental results for the GPS-based sideslip angle measurement and cornering stiffness estimates compare favorably to theoretical predictions, suggesting that this technique has merit for future implementation in vehicle safety systems.
Abstract: This paper details a unique method for estimating key vehicle states-body sideslip angle, tire sideslip angle, and vehicle attitude-using Global Positioning System (GPS) measurements in conjunction with other sensors. A method is presented for integrating Inertial Navigation System sensors with GPS measurements to provide higher update rate estimates of the vehicle states. The influence of road side-slope and vehicle roll on estimating vehicle sideslip is investigated. A method using one GPS antenna that estimates accelerometer errors occurring from vehicle roll and sensor drift is first developed. A second method is then presented utilizing a two-antenna GPS system to provide direct measurements of vehicle roll and heading, resulting in improved sideslip estimation. Additionally, it is shown that the tire sideslip estimates can be used to estimate the tire cornering stiffnesses. The experimental results for the GPS-based sideslip angle measurement and cornering stiffness estimates compare favorably to theoretical predictions, suggesting that this technique has merit for future implementation in vehicle safety systems
317 citations
TL;DR: The goals of this paper are providing an engineering argument of possible functional architectures of such systems and presenting a plausible example of the proposed future-trajectory-based design, which estimates and communicates vehicle positions and predicts and processes future trajectories for collision decision making.
Abstract: The vehicle collision warning system (CWS) is an important research and application subject for vehicle safety. Most of this topic's research focuses on autonomous CWSs, where each vehicle detects potential collisions based entirely on the information measured by itself. Recently, an alternative scenario has arisen. This scenario is known as cooperative driving, where either the vehicle or the infrastructure can communicate its location, intention, or other information to surrounding vehicles or nearby infrastructure. Since installing a low-cost global-positioning-system (GPS) unit is becoming a common practice in vehicle applications, its implications in cooperative driving and vehicle safety deserve closer investigation. Furthermore, the future trajectory prediction may lead to a straightforward approach to detect potential collisions, yet its effectiveness has not been studied. This paper explores the engineering feasibility of a future-trajectory-prediction-based cooperative CWS when vehicles are equipped with a relatively simple differential GPS unit and relatively basic motion sensors. The goals of this paper are twofold: providing an engineering argument of possible functional architectures of such systems and presenting a plausible example of the proposed future-trajectory-based design, which estimates and communicates vehicle positions and predicts and processes future trajectories for collision decision making. In this paper, common GPS problems such as blockage and multipath, as well as common communication problems such as dropout and delays, are assumed. However, specific choices of GPS devices and communication protocol or systems are not the focus of this paper
257 citations
TL;DR: A lane-detection method aimed at handling moving vehicles in the traffic scenes is proposed, which is able to robustly find the left and right boundary lines of the lane and is not affected by the passing traffic.
Abstract: A lane-detection method aimed at handling moving vehicles in the traffic scenes is proposed in this brief First, lane marks are extracted based on color information The extraction of lane-mark colors is designed in a way that is not affected by illumination changes and the proportion of space that vehicles on the road occupy Next, for vehicles that have the same colors as the lane marks, we utilize size, shape, and motion information to distinguish them from the real lane marks The mechanism effectively eliminates the influence of passing vehicles when performing lane detection Finally, pixels in the extracted lane-mark mask are accumulated to find the boundary lines of the lane The proposed algorithm is able to robustly find the left and right boundary lines of the lane and is not affected by the passing traffic Experimental results show that the proposed method works well on marked roads in various lighting conditions
221 citations
TL;DR: A review of recent developments and trends in tire/road friction modeling is presented, with attempts to provide a broad perspective of the initiatives and multidisciplinary techniques for related research.
Abstract: A proper tire friction model is essential to model overall vehicle dynamics for simulation, analysis, or control purposes since a ground vehicle's motion is primarily determined by the friction forces transferred from roads via tires. Motivated by the developments of high-performance antilock brake systems (ABSs), traction control, and steering systems, significant research efforts had been put into tire/road friction modeling during the past 40 years. In this paper, a review of recent developments and trends in this area is presented, with attempts to provide a broad perspective of the initiatives and multidisciplinary techniques for related research. Different longitudinal, lateral, and integrated tire/road friction models are examined. The associated friction-situation monitoring and control synthesis are discussed with a special emphasis on ABS design
TL;DR: A dynamic origin-destination (OD) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags is proposed.
Abstract: This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates
TL;DR: The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.
Abstract: Although much research has been done over the decades on the formulation of statistical regression models for road traffic relationships, they have been largely unsuitable due to the complexity of traffic characteristics. Traffic engineers have resorted to alternative methods such as neural networks, but despite some promising results, the difficulties in their design and implementation remain unresolved. In addition, the opaqueness of trained networks prevents understanding the underlying models. Fuzzy neural networks, which combine the complementary capabilities of both neural networks and fuzzy logic, thus constitute a more promising technique for modeling traffic flow. This paper describes the application of a specific class of fuzzy neural network known as the pseudo outer-product fuzzy neural network using the truth-value-restriction method (POPFNN-TVR) for short-term traffic flow prediction. The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.
TL;DR: Timing results show that the simplest of the proposed SIMD variants are more than twice as fast than the most complex one, while the latter still achieves real-time processing speeds, while their average accuracy is at least equal to that of publicly available non-SIMD algorithms.
Abstract: Stereo vision is an attractive passive sensing technique for obtaining three-dimensional (3-D) measurements. Recent hardware advances have given rise to a new class of real-time dense disparity estimation algorithms. This paper examines their suitability for intelligent vehicle (IV) applications. In order to gain a better understanding of the performance and the computational-cost tradeoff, the authors created a framework of real-time implementations. This consists of different methodical components based on single instruction multiple data (SIMD) techniques. Furthermore, the resulting algorithmic variations are compared with other publicly available algorithms. The authors argue that existing publicly available stereo data sets are not very suitable for the IV domain. Therefore, the authors' evaluation of stereo algorithms is based on novel realistically looking simulated data as well as real data from complex urban traffic scenes. In order to facilitate future benchmarks, all data used in this paper is made publicly available. The results from this study reveal that there is a considerable influence of scene conditions on the performance of all tested algorithms. Approaches that aim for (global) search optimization are more affected by this than other approaches. The best overall performance is achieved by the proposed multiple-window algorithm, which uses local matching and a left-right check for a robust error rejection. Timing results show that the simplest of the proposed SIMD variants are more than twice as fast than the most complex one. Nevertheless, the latter still achieves real-time processing speeds, while their average accuracy is at least equal to that of publicly available non-SIMD algorithms
TL;DR: A multilevel collision mitigation approach that allows a flexible tradeoff between potential benefit and the risk associated with driver acceptability and product liability is presented and algorithms that allow for an efficient incorporation of both sensor and prediction uncertainties are outlined.
Abstract: This paper deals with the problem of decision making in the context of forward collision mitigation system design. The authors present a multilevel collision mitigation (CM) approach that allows a flexible tradeoff between potential benefit and the risk associated with driver acceptability and product liability. Due to its practical relevance, algorithms that allow for an efficient incorporation of both sensor and prediction uncertainties are further outlined. The performance tradeoffs that come along with different parameterizations are investigated by means of stochastic simulations on three dangerous traffic situations, namely 1) rear-end collisions due to an unexpected braking, 2) cutting-in vehicles, and 3) crossing traffic at intersections. The results show that an overly conservative CM system sacrifices much of its potential benefit. However, it is pointed out that the vision of accident-free driving can be achieved only through cooperative driving strategies
TL;DR: The results from real experiments show that the unmanned vehicles behave very similarly to human-driven cars and are very adaptive to any kind of situation at a broad range of speeds, thus raising the safety of the driving and allowing cooperation with manually driven cars.
Abstract: Research on adaptive cruise control (ACC) with Stop&Go maneuvers is presently one of the most important topics in the field of intelligent transportation systems. The main feature of such controllers is that there is adaptation to a user-preset speed and, if necessary, speed reduction to keep a safe distance from the vehicle ahead in the same lane of the road, whatever the speed. The extreme case is the stop and go operation in which the lead car stops and the vehicle at the rear must also do so. This paper presents the development of an ACC system and related experiments. The system input information is acquired by a real-time kinematic phase differential global positioning system (GPS) (i.e., centimetric GPS) and wireless local area network links. The outputs are the variables that control the pressure on the throttle and brake pedals, which is calculated by an onboard computer. In addition, the car control is based on fuzzy logic. The system has been installed in two mass-produced Citroe/spl uml/n Berlingo electric vans, in which all the actuators have been automated to achieve humanlike driving. The results from real experiments show that the unmanned vehicles behave very similarly to human-driven cars and are very adaptive to any kind of situation at a broad range of speeds, thus raising the safety of the driving and allowing cooperation with manually driven cars.
TL;DR: A crash-likelihood prediction model using real-time traffic-flow variables and rain data potentially associated with crash occurrence and a matched case-control logit model has been used to model the crash potential based on traffic loop data and the rain index.
Abstract: Growing concern over traffic safety has led to research efforts directed towards predicting freeway crashes in Advanced Traffic Management and Information Systems (ATMIS) environment. This paper aims at developing a crash-likelihood prediction model using real-time traffic-flow variables (measured through series of underground sensors) and rain data (collected at weather stations) potentially associated with crash occurrence. Archived loop detector and rain data and historical crash data have been used to calibrate the model. This model can be implemented using an online loop and rain data to identify high crash potential in real-time. Principal component analysis (PCA) and logistic regression (LR) have been used to estimate a weather model that determines a rain index based on the rain readings at the weather station in the proximity of the freeway. A matched case-control logit model has also been used to model the crash potential based on traffic loop data and the rain index. The 5-min average occupancy and standard deviation of volume observed at the downstream station, and the 5-min coefficient of variation in speed at the station closest to the crash, all during 5-10 min prior to the crash occurrence along with the rain index have been found to affect the crash occurrence most significantly.
TL;DR: Different computation methods with increasing complexity are provided and the necessity to take into consideration vehicle dynamics to use the TLC as a lane departure indicator is outlined.
Abstract: The main goal of this paper is to develop a distance to line crossing (DLC) based computation of time to line crossing (TLC). Different computation methods with increasing complexity are provided. A discussion develops the influence of assumptions generally assumed for approximation. A sensitivity analysis with respect to vehicle parameters and positioning is performed. For TLC computation, both straight and curved vehicle paths are considered. The road curvature being another important variable considered in the proposed computations, an observer for its estimation is then proposed. An evaluation over a digitalized test track is first performed. Real data are then collected through an experiment carried out in test tracks with the equipped prototype vehicle. Based on these real data, TLC is then computed with the theoretically proposed methods. The obtained results outlined the necessity to take into consideration vehicle dynamics to use the TLC as a lane departure indicator.
TL;DR: A framework for conflict resolution that allows one to take into account such levels of uncertainty using a stochastic simulator is presented and it is shown how the cost criterion can be selected to ensure an upper bound on the probability of conflict for the optimal maneuver.
Abstract: The safety of flights, and, in particular, separation assurance, is one of the main tasks of air traffic control (ATC). Conflict resolution refers to the process used by ATCs to prevent loss of separation. Conflict resolution involves issuing instructions to aircraft to avoid loss of safe separation between them and, at the same time, direct them to their destinations. Conflict resolution requires decision making in the face of the considerable levels of uncertainty inherent in the motion of aircraft. In this paper, a framework for conflict resolution that allows one to take into account such levels of uncertainty using a stochastic simulator is presented. The conflict resolution task is posed as the problem of optimizing an expected value criterion. It is then shown how the cost criterion can be selected to ensure an upper bound on the probability of conflict for the optimal maneuver. Optimization of the expected value resolution criterion is carried out through an iterative procedure based on Markov chain Monte Carlo. Simulation examples inspired by current ATC practice in terminal maneuvering areas and approach sectors illustrate the proposed conflict resolution strategy
TL;DR: A low-level object tracking system that produces accurate vehicle motion trajectories that can be further analyzed to detect lane centers and classify lane types is described.
Abstract: Intelligent vision-based traffic surveillance systems are assuming an increasingly important role in highway monitoring and road management schemes. This paper describes a low-level object tracking system that produces accurate vehicle motion trajectories that can be further analyzed to detect lane centers and classify lane types. Accompanying techniques for indexing and retrieval of anomalous trajectories are also derived. The predictive trajectory merge-and-split algorithm is used to detect partial or complete occlusions during object motion and incorporates a Kalman filter that is used to perform vehicle tracking. The resulting motion trajectories are modeled using variable low-degree polynomials. A K-means clustering technique on the coefficient space can be used to obtain approximate lane centers. Estimation bias due to vehicle lane changes can be removed using robust estimation techniques based on Random Sample Consensus (RANSAC). Through the use of nonmetric distance functions and a simple directional indicator, highway lanes can be classified into one of the following categories: entry, exit, primary, or secondary. Experimental results are presented to show the real-time application of this approach to multiple views obtained by an uncalibrated pan-tilt-zoom traffic camera monitoring the junction of two busy intersecting highways.
TL;DR: These experiments prove the basic feasibility and show promise of omni-camera-based DPS capture algorithm to provide useful semantic descriptors of the state of moving vehicles and obstacles in the vicinity of a vehicle.
Abstract: Awareness of what surrounds a vehicle directly affects the safe driving and maneuvering of an automobile. This paper focuses on the capture of vehicle surroundings using video inputs. Surround information or maps can help in studies of driver behavior as well as provide critical input in the development of effective driver assistance systems. A survey of literature related to surround analysis is presented, emphasizing detecting objects such as vehicles, pedestrians, and other obstacles. Omni cameras, which give a panoramic view of the surroundings, can be useful for visualizing and analyzing the nearby surroundings of the vehicle. The concept of Dynamic Panoramic Surround (DPS) map that shows the nearby surroundings of the vehicle and detects the objects of importance on the road is introduced. A novel approach for synthesizing the DPS using stereo and motion analysis of video images from a pair of omni cameras on the vehicle is developed. Successful generation of the DPS in experimental runs on an instrumented vehicle test bed is demonstrated. These experiments prove the basic feasibility and show promise of omni-camera-based DPS capture algorithm to provide useful semantic descriptors of the state of moving vehicles and obstacles in the vicinity of a vehicle
TL;DR: Possible benefits trucks may have in mixed traffic are indicated and what is already known-that trucks could be detrimental to traffic flow is reinforced.
Abstract: In this paper, longitudinal vehicle-following controllers for heavy trucks with different spacing policies are designed, analyzed, simulated, and experimentally tested, and their performance in mixed traffic with passenger vehicles is evaluated. A new vehicle-following controller for trucks, which has better properties than existing ones with respect to performance and impact on fuel economy and pollution during traffic disturbances, is developed. The response of trucks to disturbances caused by lead passenger vehicles is smooth due to the limited acceleration capabilities of trucks whether they are manual or equipped with adaptive cruise control (ACC) systems. Vehicles following the truck are therefore presented with a smoother speed trajectory to track. This filtering effect of trucks is shown to have beneficial effects on fuel economy and pollution. However, it creates large intervehicle gaps that invite cut-ins from neighboring lanes, creating additional disturbances. These cut-ins, under certain realistic scenarios, may reduce any benefits obtained by the smooth response of trucks as well as increase travel time. The results of this paper indicate possible benefits trucks may have in mixed traffic and also reinforces what is already known-that trucks could be detrimental to traffic flow
TL;DR: A formulation of the conflict avoidance problem as a mixed-integer nonlinear-programming problem is proposed and, in the author's case, only velocity changes are admissible as maneuvers.
Abstract: In this paper, optimal resolution of air-traffic (AT) conflicts were considered. Aircraft are assumed to cruise within a free altitude layer and are modeled in three dimensions with variable velocity and proximity bounds. Aircraft cannot get closer to each other than a predefined safety distance. The problem of solving conflicts arising among several aircraft that are assumed to move in a shared airspace were considered. For such systems of multiple aircraft, the total flight time by avoiding all possible conflicts were minimized. This paper proposes a formulation of the conflict avoidance problem as a mixed-integer nonlinear-programming problem. In the author's case, only velocity changes are admissible as maneuvers. Nevertheless, in subsequent work, simultaneous velocity and heading angle changes will be checked too. Simulation results for realistic aircraft conflict scenarios are provided.
TL;DR: The authors present a technique to estimate the mobilized visibility distance through a use of onboard charge-coupled device cameras that is operative night and day in every kind of meteorological condition and is evaluated.
Abstract: An atmospheric visibility measurement system capable of quantifying the most common operating range of onboard exteroceptive sensors is a key parameter in the creation of driving assistance systems. This information is then utilized to adapt sensor operations and processing or to alert the driver that his onboard assistance system is momentarily inoperative. Moreover, a system capable of either detecting the presence of fog or estimating visibility distances constitutes in itself a driving assistance. In this paper, the authors present a technique to estimate the mobilized visibility distance through a use of onboard charge-coupled device cameras. The latter represents the distance to the most distant object on the road surface having a contrast above 5%. This definition is very close to the definition of the meteorological visibility distance proposed by the International Commission on Illumination. The method combines the computations of local contrasts above 5% and of a depth map of the vehicle environment using stereovision within 60 ms on a current-day computer. In this paper, both methods are described separately. Then, their combination is detailed. The method is operative night and day in every kind of meteorological condition and is evaluated; thanks to video sequences under sunny weather and foggy weather.
TL;DR: It appears that the trainable similarity representation alleviates some difficulties of other algorithms that are currently used in road-sign classification, including a multiclass classification accuracy, nonsign rejection capability and computational demands in execution.
Abstract: Deriving an informative data representation is an important prerequisite when designing road-sign classifiers. A frequently used strategy for road-sign classification is based on the normalized cross correlation similarity to class prototypes followed by the nearest neighbor classifier. Because of the global nature of the cross correlation similarity, this method suffers from presence of uninformative pixels (caused, e.g., by occlusions) and is computationally demanding. In this paper, a novel concept of a trainable similarity measure is introduced, which alleviates these shortcomings. The similarity is based on individual matches in a set of local image regions. The set of regions that are relevant for a particular similarity assessment is refined by the training process. It is illustrated on a set of experiments with road-sign-classification problems that the trainable similarity yields high-performance data representations and classifiers. Apart from a multiclass classification accuracy, nonsign rejection capability and computational demands in execution are also discussed. It appears that the trainable similarity representation alleviates some difficulties of other algorithms that are currently used in road-sign classification
TL;DR: Experimental trials using robots having limited and directional perception of other things, using vision and obstacle avoidance sensing confirm the feasibility of the coordination strategies in different conditions and various uses of communicated information to compensate for sensing limitations.
Abstract: To eventually have automated vehicles operate in platoons, it is necessary to study what information each vehicle must have and to whom it must communicate for safe and efficient maneuvering in all possible conditions. This paper formulates the problem in terms of sensing and communicated information. By emulating platoons using a group of mobile robots, the authors demonstrate the feasibility of maneuvers (such as entering, exiting, and recuperating from an accident) using different distributed coordination strategies. The coordination strategies studied range from no communication to unidirectional or bidirectional exchanges between vehicles and to fully centralized decision by the leading vehicle. One particularity of this paper is that instead of assuming that the platoon leader or all vehicles globally monitor what is going on, only the vehicles involved in a particular maneuver are concerned, distributing decisions locally among the platoon. This paper reports experimental trials using robots having limited and directional perception of other things, using vision and obstacle avoidance sensing. Results confirm the feasibility of the coordination strategies in different conditions and various uses of communicated information to compensate for sensing limitations
TL;DR: A neural network (NN) adaptive model-based combined lateral and longitudinal vehicle control algorithm for highway applications is presented in this paper and can guarantee the uniform ultimate bounds of the tracking errors and bounds of NN weights.
Abstract: A neural network (NN) adaptive model-based combined lateral and longitudinal vehicle control algorithm for highway applications is presented in this paper. The controller is synthesized using a proportional plus derivative control coupled with an online adaptive neural module that acts as a dynamic compensator to counteract inherent model discrepancies, strong nonlinearities, and coupling effects. The closed-loop stability issues of this combined control scheme are analyzed using a Lyapunov-based method. The neurocontrol approach can guarantee the uniform ultimate bounds of the tracking errors and bounds of NN weights. A complex nonlinear three-degree-of-freedom dynamic model of a passenger wagon is developed to simulate the vehicle motion and for controller design. The controller is tested and verified via computer simulations in the presence of parametric uncertainties and severe driving conditions
TL;DR: The authors estimated five models to address drivers' diversion, compliance, and route choice and showed that travel time and familiarity with the device that provides the information had significant effects on the first four models.
Abstract: The objective of this paper is to collect and analyze data that can be used to model mode- and different route-choice paradigms using same subjects and same experiment. In this paper, the authors estimated five models to address drivers' diversion, compliance, and route choice. In addition, the effect of advanced traveler information systems (ATIS) on the mode choice is also considered. A travel simulator was used as a dynamic data collection tool. The simulator uses a realistic network, two modes of travel, real historical volumes, and different weather conditions. It provides five different levels of traffic information/advice, one at a time, and collects dynamic mode choices and pretrip (long-term) and en-route (short-term) route choices. The binomial and multinomial generalized extreme equations (BGEE and MGEE) were used to account for the correlation between repeated choices made by the same subject. In addition, MGEE accounts for the correlation between alternatives in multidimensional route-choice models. The modeling results showed that travel time and familiarity with the device that provides the information had significant effects on the first four models. It is shown that developing in-vehicle information devices may lead to a less transit usage in some cases; this indicates a potential drawback of this technology. Expressway users are shown as the most travel-time savers who would divert if they are guided to a less-travel-time alternative. The number of traffic signals on the normal and advised routes affects the diversion from the normal route and compliance with the pretrip advised route. This paper underlines the importance of modeling correlation, if it exists, in mode/route-choice data
TL;DR: A methodology is developed to estimate the truck arrival time at each customer location and an approximate solution method based on dynamic programming is proposed that finds the best route with minimum expected cost while it guarantees certain levels of service are met.
Abstract: Most existing methods for truck route planning assume known static data in an environment that is time varying and uncertain by nature, which limits their widespread applicability. The development of intelligent transportation systems such as the use of information technologies reduces the level of uncertainties and makes the use of more appropriate dynamic formulations and solutions feasible. In this paper, a truck route planning problem called stochastic traveling salesman problem with time windows (STSPTW) in which traveling times along roads and service times at customer locations are stochastic processes is investigated. A methodology is developed to estimate the truck arrival time at each customer location. Using estimated arrival times, an approximate solution method based on dynamic programming is proposed. The algorithm finds the best route with minimum expected cost while it guarantees certain levels of service are met. Simulation results are used to demonstrate the efficiency of the proposed algorithm