Current Lane Research Articles

Reliable road vehicle collision prediction with constrained filtering

The reliability of collision prediction for host vehicle-based collision warning systems relies heavily on the accuracy of estimated future paths of target vehicle kinematics. This paper presents a constrained filtering scheme that incorporates roadway information into a radar-based vehicle tracking process. Our proposed method makes use of the geometric structure of the road (the clothoid model) to form constraints on target vehicle kinematical variables. These constraints are used to generate an independent estimation of a state variable subset, and the inclusion of the constraints into a Kalman filter is materialized by a fusion process, which yields current state estimates. With the assistance of unconstrained filtering, a hypothesis testing scheme is performed every sampling instant to remove constraints when the target vehicle is not following the road or is changing lanes. A multiple constraint scheme is used to determine the current lane of the target vehicle, which enables a lane adaptive hypothesis testing procedure. Simulation shows that the proposed method can effectively reduce estimation errors and improve the prediction of in-lane collisions without degrading the prediction of cross-lane collisions.

Lane-Changing Model with Explicit Target Lane Choice

The lane-changing model is an important component of microscopic traffic simulation tools. With the increasing popularity of these tools, a number of lane-changing models have been proposed and implemented in various simulators in recent years. Most of these models are based on the assumption that drivers evaluate the current and adjacent lanes and choose a direction of change (or no change) on the basis of the utilities of these lanes only. The lane choice set is therefore dictated by the current position of the vehicle and in multilane facilities would be restricted to a subset of the available lanes. Thus, existing models lack an explicit tactical choice of a target lane and therefore cannot explain a sequence of lane changes from the current lane to this lane. In this paper, a generalized lane-changing model that explicitly incorporates the choice of target lane is presented. The target lane is the lane that the driver perceives to be the best when a wide range of factors and goals are taken into account. The immediate direction in which a driver changes lanes is determined by the target lane choice. All parameters of the model were jointly estimated with detailed vehicle trajectory data. The model was validated and compared with an existing lane-changing model with the use of a microscopic traffic simulator. The results indicate that the proposed model performs significantly better than the previous model.

Lane-Changing Model with Explicit Target Lane Choice

The lane-changing model is an important component of microscopic traffic simulation tools. With the increasing popularity of these tools, a number of lane-changing models have been proposed and implemented in various simulators in recent years. Most of these models are based on the assumption that drivers evaluate the current and adjacent lanes and choose a direction of change (or no change) on the basis of the utilities of these lanes only. The lane choice set is therefore dictated by the current position of the vehicle and in multilane facilities would be restricted to a subset of the available lanes. Thus, existing models lack an explicit tactical choice of a target lane and therefore cannot explain a sequence of lane changes from the current lane to this lane. In this paper, a generalized lane-changing model that explicitly incorporates the choice of target lane is presented. The target lane is the lane that the driver perceives to be the best when a wide range of factors and goals are taken into account. The immediate direction in which a driver changes lanes is determined by the target lane choice. All parameters of the model were jointly estimated with detailed vehicle trajectory data. The model was validated and compared with an existing lane-changing model with the use of a microscopic traffic simulator. The results indicate that the proposed model performs significantly better than the previous model.

Modeling Integrated Lane-Changing Behavior

The lane-changing model is an important component within microscopic traffic simulation tools. Following the emergence of these tools in recent years, interest in the development of more reliable lane-changing models has increased. Lane-changing behavior is also important in several other applications such as capacity analysis and safety studies. Lane-changing behavior is usually modeled in two steps: ( a) the decision to consider a lane change, and ( b) the decision to execute the lane change. In most models, lane changes are classified as either mandatory (MLC) or discretionary (DLC). MLC are performed when the driver must leave the current lane. DLC are performed to improve driving conditions. Gap acceptance models are used to model the execution of lane changes. The classification of lane changes as either mandatory or discretionary prohibits capturing trade-offs between these considerations. The result is a rigid behavioral structure that does not permit, for example, overtaking when mandatory considerations are active. Using these models within a microsimulator may result in unrealistic traffic flow characteristics. In addition, little empirical work has been done to rigorously estimate the parameters of lane-changing models. An integrated lane-changing model, which allows drivers to jointly consider mandatory and discretionary considerations, is presented. Parameters of the model are estimated with detailed vehicle trajectory data.

Un scénario périurbain de route automatisée

A scenario for a suburban automated highway with the objective of reducing congestion is described. Starting from a reference situation similar to a current traffic lane, the authors consider the influence of a number of parameters on the achievable capacity of an automated section of road. These parameters are used to estimate safe following distances which are computed with the aim of avoiding collisions in the event of sudden braking. They then consider the number of collisions in the presence of a brick wall type obstacle on the carriageway. Ramp metering based on a macroscopic approach leads to a loss of capacity, which we overcome by implementing a management technique that integrates individual vehicles in the stream. Lastly, a general architecture for the scenario with several control and co-ordination layers is proposed.

Observation-Based Lane-Vehicle Assignment Hierarchy: Microscopic Simulation on Urban Street Network

A lane-assignment model in a vehicle-based microscopic simulation system describes a vehicle’s position during its journey on an urban street network. In other words, it is used to estimate an individual vehicle’s location, speed, routing plan, lane-choice plan, lane-changing plan, and car-following plan from its entrance to a street network until the end of the trip. From the authors’ observations and study of lanechoice and lane-changing behavior, it is concluded that a vehicle is assigned to a lane in a logical manner depending on the relationship between its route-planned motivation and traffic conditions in the current lane and other lanes. A lane-assignment model consists of three components: lane choice, car following, and lane changing. The lane-changing component is composed of three submodels—a decision model, a lane-changing condition model, and a lane-changing maneuver model. Rules are discussed for lane-choice and lane-changing modeling based on videotaped observations over four-lane urban streets. Then a heuristic structure of a lane-vehicle-assignment model is proposed, which exposes the inherent relationship between vehicle-based travel behavior and lane-vehicle assignment on an urban street network. With the addition of a lane-assignment model derived from observed data, a simulation may be developed to correctly represent travel behavior and dynamic traffic assignment at the lane level and provide a more effective tool for design and evaluation of the performance of strategies for traffic control, traveler information, and congestion alleviation.

Characterizing and Modeling Observed Lane-Changing Behavior: Lane-Vehicle-Based Microscopic Simulation on Urban Street Network

Key findings are discussed regarding characteristics of lane-changing behavior based on observations of an urban street network. An in-depth exploration of observed lane-changing behavior and its modeling were conducted using vehicle trajectory data extracted from video observations using VEVID, a software package developed by the authors, integrated with a video-capture system. As a result, rules for modeling lane-changing behavior are proposed with respect to various types of lane changes. A lane-changing model consists of three components: a decision model, a condition model, and a maneuver model. Drivers’ decisions to change lanes depend on travel maneuver plans, the current lane type (i.e., the relationship between the current lane and the driver’s planned route), and traffic conditions in the current and adjacent lanes. A lane-changing condition model is the description of acceptable conditions for different types of lane changes. A lane-changing maneuver model describes a vehicle’s speed and duration when a certain type of lane change occurs. All of these models are established in a heuristic structure.

Road Recognition in Complex Traffic Situations

This paper describes the algorithms for road edge detection in complex traffic situations by an on-board video camera. The information that comes from the motion of a dynamic scene is used to build a new family of filters called Dynamic Time Filters (DTF). These filters apply morphological opening and closing transformations to the image sequence provided by the camera. This sequence is considered as a 3D image. The DTF-filtered image is segmented by means of the watershed transformation. Two different modes have been implemented for the segmentation. The acquisition mode allows an initial extraction of the current traffic lane. It also produces initial markers which will be used by the second segmentation mode, where an interpolated road/lane model is used to follow and segment the lanes.Some results of the segmentation process are given in complex situations to illustrate the efficiency and robustness of this approach. Information is also given on the real-time performance of the algorithms implemented on a car demonstrator.

Distribution of Screwworms (Diptera: Calliphoridae) Relative to Land Use and Topography in the Humid Tropics of Southern Mexico

The distribution of adult screwworms ( Cochliomyia hominivorax [Coquillet]) relative to land use and topography was studied on the humid Pacific coastal plain of southern Chiapas, Mexico. Native screwworms were always more abundant when windoriented traps, baited with the attractant swormlure-4, were upwind and within 150 m of warm-blooded animals. Densities of flies were greatest in ecotones that included pastures and any tall vegetation. Flies also were abundant near houses with domestic animals. Flies were least common in low-profile stands (<0.75 m high) of young banana, young soybean, and pastures without animals. These data indicate that habitat preferences exist, that they are easily identifiable by land use, and that trapping efficiency can be improved by placing traps near and upwind of warm-blooded animals. The habitat preferences of sterile screwworms (strain M-83), released from a central ground release station, were similar to those of native flies within the study area; sterile and native males showed similar distributions to females. The data indicate that the dispersal of sterile flies within 2.1 km of the release point was adequate, and that current flight lane patterns for aerially releasing sterile screwworms appear adequate in southern Mexico.