In this paper, we present a novel model to detect lane regions and extract lane departure events (changes and incursions) from challenging, lower-resolution videos recorded with mobile cameras. Our algorithm used a Mask-RCNN based lane detection model as pre-processor. Recently, deep learning-based models provide state-of-the-art technology for object detection combined with segmentation. Among the several deep learning architectures, convolutional neural networks (CNNs) outperformed other machine learning models, especially for region proposal and object detection tasks. Recent development in object detection has been driven by the success of region proposal methods and region-based CNNs (R-CNNs). Our algorithm utilizes lane segmentation mask for detection and Fix-lag Kalman filter for tracking, rather than the usual approach of detecting lane lines from single video frames. The algorithm permits detection of driver lane departures into left or right lanes from continuous lane detections. Preliminary results show promise for robust detection of lane departure events. The overall sensitivity for lane departure events on our custom test dataset is 81.81 .
The TLC model has been extensively used on production vehicles @cite_21 . TLC systems evaluate the lane and vehicle state relying on vision-based equipment and perform TLC calculations online using a variety of algorithms. A TLC threshold is used to trigger an alert to the driver. Different computational methods are used with regard to the road geometries and vehicle types. Among these methods, the most common method used is to predict the road boundary, the vehicle trajectory, and then calculate intersection time of the two at the current driving speed. On small curvature roads, the TLC can be computed as the ratio of lateral distance to lateral velocity or the ratio of the distance to the line crossing. @cite_20 Studies suggest that TLC tend to have a higher false alarm rate (FAR) when the vehicle is driven close to lane boundary @cite_17 @cite_0 .
@cite_0 @cite_21 @cite_20 @cite_17
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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. Misunderstanding of driver correction behaviors is the primary reason for false warnings of lane-departure-prediction systems. We proposed a learning-based approach to predict unintended lane-departure behaviors and chances of drivers to bring vehicles back to the lane. First, a personalized driver model for lane-departure and lane-keeping behavior is established by combining the Gaussian mixture model and the hidden Markov model. Second, based on this model, we developed an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will act a lane departure behavior or correction behavior. We also develop a warning strategy based on the model-based prediction algorithm that allows the lane-departure warning system to be acceptable for drivers according to the predicted trajectory. In addition, the naturalistic driving data of ten drivers were collected to train the personalized driver model and validate this approach. We compared the proposed method with a basic time-to-lane-crossing (TLC) method and a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. Experimental results show that the proposed approach can reduce the false-warning rate to 3.13 on average at 1-s prediction time. In this paper, a technique for the identification of the unwanted lane departure of a traveling vehicle on a road is proposed. A piecewise linear stretching function (PLSF) is used to improve the contrast level of the region of interest (ROI). Lane markings on the road are detected by dividing the ROI into two subregions and applying the Hough transform in each subregion independently. This segmentation approach improves the computational time required for lane detection. For lane departure identification, a distance-based departure measure is computed at each frame, and a necessary warning message is issued to the driver when such measure exceeds a threshold. The novelty of the proposed algorithm is the identification of the lane departure only using three lane-related parameters based on the Euclidean distance transform to estimate the departure measure. The use of the Euclidean distance transform in combination with the PLSF keeps the false alarm around 3 and the lane detection rate above 97 under various lighting conditions. Experimental results indicate that the proposed system can detect lane boundaries in the presence of several image artifacts, such as lighting changes, poor lane markings, and occlusions by a vehicle, and it issues an accurate lane departure warning in a short time interval. The proposed technique shows the efficiency with some real video sequences. 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 Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. The challenge of cooperative driving is estimating other vehicles' intentions. In this paper, we present a novel method to estimate other human-driven vehicles' intentions with the aim of achieving a natural and amenable cooperative driving behavior, without using wireless communication. The new approach allows the autonomous vehicle to cooperate with multiple observable merging vehicles on the ramp with a leading vehicle ahead of the autonomous vehicle in the same lane. To avoid calculating trajectories, simplify computation, and take advantage of mature Level-3 components, the new method reacts to merging cars by determining a following target for an off-the-shelf distance keeping module (ACC) which governs speed control of the autonomous vehicle. We train and evaluate the proposed model using real traffic data. Results show that the new approach has a lower collision rate than previous methods and generates more human driver-like behaviors in terms of trajectory similarity and time-to-collision to leading vehicles. Detecting the road area and ego-lane ahead of a vehicle is central to modern driver assistance systems. While lane-detection on well-marked roads is already available in modern vehicles, finding the boundaries of unmarked or weakly marked roads and lanes as they appear in inner-city and rural environments remains an unsolved problem due to the high variability in scene layout and illumination conditions, amongst others. While recent years have witnessed great interest in this subject, to date no commonly agreed upon benchmark exists, rendering a fair comparison amongst methods difficult. In this paper, we introduce a novel open-access dataset and benchmark for road area and ego-lane detection. Our dataset comprises 600 annotated training and test images of high variability from the KITTI autonomous driving project, capturing a broad spectrum of urban road scenes. For evaluation, we propose to use the 2D Bird's Eye View (BEV) space as vehicle control usually happens in this 2D world, requiring detection results to be represented in this very same space. Furthermore, we propose a novel, behavior-based metric which judges the utility of the extracted ego-lane area for driver assistance applications by fitting a driving corridor to the road detection results in the BEV. We believe this to be important for a meaningful evaluation as pixel-level performance is of limited value for vehicle control. State-of-the-art road detection algorithms are used to demonstrate results using classical pixel-level metrics in perspective and BEV space as well as the novel behavior-based performance measure. All data and annotations are made publicly available on the KITTI online evaluation website in order to serve as a common benchmark for road terrain detection algorithms. A novel algorithm considering both time-to-collision (TTC) and safety braking distance for forward collision warning system is presented to alert and to assist a driver in keeping safety braking distance to avoid the collision accident in highway. We use the Artificial Neural Network (ANN) to predict the safety braking distance and TCC based on the most important parameters, which are the distance between the driving car and the vehicle (obstacle) ahead, the variable of the distance between the driving car and the vehicle (obstacle) ahead, vehicle weight, vehicle speed, slope of road, condition of road surface and the age of driver. The system compares the distance of vehicle (obstacle) ahead and safety braking distance and also determines whether the moving vehicle’s safety braking distance is enough or not. The reaction time of driver and pressure build-up time of braking system are all taken into account. The useful alert messages can serve as a safety assistance system for safer driving in highway. We propose a new detection method to predict a vehicle's trajectory and use it for detecting lane changes of surrounding vehicles. According to the previous research, more than 90 of the car crashes are caused by human errors, and lane changes are the main factor. Therefore, if a lane change can be detected before a vehicle crosses the centerline, accident rates will decrease. Previously reported detection methods have the problem of frequent false alarms caused by zigzag driving that can result in user distrust in driving safety support systems. Most cases of zigzag driving are caused by the abortion of a lane change due to the presence of adjacent vehicles on the next lane. Our approach reduces false alarms by considering the possibility of a crash with adjacent vehicles by applying trajectory prediction when the target vehicle attempts to change a lane, and it reflects the result of lane-change detection. We used a traffic dataset with more than 500 lane changes and confirmed that the proposed method can considerably improve the detection performance. This paper proposes a method for achieving improved ego-vehicle global localization with respect to an approaching intersection, which is based on the alignment of visual landmarks perceived by the on-board visual system, with the information from a proposed extended digital map (EDM). The visual system relies on a stereovision system that provides a detailed 3-D description of the environment, including road landmark information (lateral lane delimiters, painted traffic signs, curbs, and stop lines) and dynamic environment information (other vehicles). An EDM is proposed, which enriches the standard map information with a detailed description of the intersection required for current lane identification, landmark alignment, and ego-vehicle accurate global localization. A novel approach for lane-delimiter classification, which is necessary for the lane identification, is also presented. An original solution for identifying the current lane, combining visual and map information with the help of a Bayesian network (BN), is proposed. Extensive experiments have been performed, and the results are evaluated with a Global Navigation Satellite System of high accuracy (2 cm). The achieved global localization accuracy is of submeter level, depending on the performance of the stereovision system. This paper presents a lane-departure identification (LDI) system of a traveling vehicle on a structured road with lane marks. As is the case with modified version of the previous EDF-based LDI approach [J.W. Lee, A machine vision system for lane-departure detection, CVIU 86 (2002) 52-78], the new system increases the number of lane-related parameters and introduces departure ratios to determine the instant of lane departure and a linear regression (LR) to minimize wrong decisions due to noise effects. To enhance the robustness of LDI, we conceive of a lane boundary pixel extractor (LBPE) capable of extracting pixels expected to be on lane boundaries. Then, the Hough transform utilizes the pixels from the LBPE to provide the lane-related parameters such as an orientation and a location parameter. The fundamental idea of the proposed LDI is based on an observation that the ratios of orientations and location parameters of left- and right-lane boundaries are equal to one as far as the optical axis of a camera mounted on a vehicle is coincident with the center of lane. The ratios enable the lane-related parameters and the symmetrical property of both lane boundaries to be connected. In addition, the LR of the lane-related parameters of a series of successive images plays the role of determining the trend of a vehicle's traveling direction and the error of the LR is used to avoid a wrong LDI. We show the efficiency of the proposed LDI system with some real images. For safe and reliable autonomous driving systems, prediction of surrounding vehicles' future behavior and potential risks are critical. The state-of-the-art prediction algorithms tend to show limited performance on long-term predictions due to their deterministic nature. In this paper, a probabilistic lateral motion prediction algorithm is proposed based on multilayer perceptron (MLP) approach. The MLP model consists of two parts; target lane and trajectory models. In order to develop an intuitive and accurate prediction algorithm, a lane-based trajectory prediction model is introduced based on the fact that vehicles drive within a lane except for during lane changes. More specifically, a set of three representative trajectories with different levels of lane-change positions are generated for each target lane, and real-world traffic data is categorized by each trajectory for MLP training. These target lane and trajectory models enable the stochastic MLP modeling and training. The proposed MLP model outputs probabilities of how likely a vehicle will follow each trajectory and each lane for a given input of vehicle position history including current position. For training the MLP model, Next Generation Simulation traffic data are used. Simulation results show that the proposed algorithm detects lane-changes one to one and a half second earlier than existing methods and three seconds before lane crossing with about ninety percentages accuracy. XML is widely praised for its flexibility in allowing repeated and missing sub-elements. However, this flexibility makes it challenging to develop a bulk algebra, which typically manipulates sets of objects with identical structure. A set of XML elements, say of type book, may have members that vary greatly in structure, e.g. in the number of author sub-elements. This kind of heterogeneity may permeate the entire document in a recursive fashion: e.g., different authors of the same or different book may in turn greatly vary in structure. Even when the document conforms to a schema, the flexible nature of schemas for XML still allows such significant variations in structure among elements in a collection. Bulk processing of such heterogeneous sets is problematic.In this paper, we introduce the notion of logical classes (LC) of pattern tree nodes, and generalize the notion of pattern tree matching to handle node logical classes. This abstraction pays off significantly in allowing us to reason with an inherently heterogeneous collection of elements in a uniform, homogeneous way. Based on this, we define a Tree Logical Class (TLC) algebra that is capable of handling the heterogeneity arising in XML query processing, while avoiding redundant work. We present an algorithm to obtain a TLC algebra expression from an XQuery statement (for a large fragment of XQuery). We show how to implement the TLC algebra efficiently, introducing the nest-join as an important physical operator for XML query processing. We show that evaluation plans generated using the TLC algebra not only are simpler but also perform better than those generated by competing approaches. TLC is the algebra used in the T imber [8] system developed at the University of Michigan. We present an autonomous driving research vehicle with minimal appearance modifications that is capable of a wide range of autonomous and intelligent behaviors, including smooth and comfortable trajectory generation and following; lane keeping and lane changing; intersection handling with or without V2I and V2V; and pedestrian, bicyclist, and workzone detection. Safety and reliability features include a fault-tolerant computing system; smooth and intuitive autonomous-manual switching; and the ability to fully disengage and power down the drive-by-wire and computing system upon E-stop. The vehicle has been tested extensively on both a closed test field and public roads. This paper describes the generic obstacle and lane detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an on-board monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement. This paper describes a system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in real-time in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior. Our method is notable in several respects: it detects and estimates multiple travel lanes; it fuses asynchronous, heterogeneous sensor streams; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the road. We analyze the system's performance in the context of the 2007 DARPA Urban Challenge. With five cameras and thirteen lidars, our method was incorporated into a closed-loop controller to successfully guide an autonomous vehicle through a 90 km urban course at speeds up to 40 km h amidst moving traffic. Predicting other traffic participants trajectories is a crucial task for an autonomous vehicle, in order to avoid collisions on its planned trajectory. It is also necessary for many Advanced Driver Assistance Systems, where the ego-vehicle's trajectory has to be predicted too. Even if trajectory prediction is not a deterministic task, it is possible to point out the most likely trajectory. This paper presents a new trajectory prediction method which combines a trajectory prediction based on Constant Yaw Rate and Acceleration motion model and a trajectory prediction based on maneuver recognition. It takes benefit on the accuracy of both predictions respectively a short-term and long-term. The defined Maneuver Recognition Module selects the current maneuver from a predefined set by comparing the center lines of the road's lanes to a local curvilinear model of the path of the vehicle. The overall approach was tested on prerecorded human real driving data and results show that the Maneuver Recognition Module has a high success rate and that the final trajectory prediction has a better accuracy. The authors propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. The authors first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the immanent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) - a generalization of the common Time-To-Collision (TTC) in arbitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class. Lane relative vehicle navigation and control requires accurate lane-relative positioning of the vehicle. This relative position can be computed by comparing the vehicle absolute position with analytic roadway maps, which requires both high-accuracy positioning of the vehicle and high-accuracy lane maps. Carrier Phase Differential GPS (CPDGPS) aided INS or CPDGPS aided encoders is capable of estimating vehicle absolute position (relative to earth center) with centimeter level accuracy; however, to the best of the author's knowledge, the accuracy of lane level maps is currently not sufficient. In this paper, we first consider the structure of lane level maps that are compatible with standard practices of GIS road modeling. Then, various analytic lane definition are discussed. We also present a method of building lane level maps from high-accuracy positioning data along the lane center. The data is segmented according to road intersections. Shape points (vertices) as a function of arclength are located based on changes in estimated curvature. For each segment, the parameters are estimated by least-square criteria and can be refined as new datasets become available. This process is shown by an example. The ALVINN (Autonomous Land Vehicle In a Neural Network) project addresses the problem of training artificial neural networks in real time to perform difficult perception tasks. ALVINN is a backpropagation network designed to drive the CMU Navlab, a modified Chevy van. This paper describes the training techniques that allow ALVINN to learn in under 5 minutes to autonomously control the Navlab by watching the reactions of a human driver. Using these techniques, ALVINN has been trained to drive in a variety of circumstances including single-lane paved and unpaved roads, and multilane lined and unlined roads, at speeds of up to 20 miles per hour. In this paper, we introduce an algorithm for generating collision-free two-lane paths for unmanned vehicles used at mining sites by using quartic (degree 4) B-spline curves. Given the boundary geometry of the haul road area and the positions and orientations of the two-dimensional vehicle at the start and goal points, the algorithm automatically generates a collision-free two-lane path that satisfies the minimum turning radius constraint. Moreover, the resulting path shares the same third derivative at knots (joints), i.e., @math continuity, which guarantees a continuous rate of change of the curvature along the entire path. Examples are provided to demonstrate the effectiveness of the proposed algorithm.