A Review of Lateral Driver Support Systems
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paper_title: Vision-based lane recognition under adverse weather conditions using optical flow

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Lane recognition is the basis for many driver assistance systems, including lane departure warning (LDW), the assignment of vehicles to specific lanes and fully autonomous driving. A major problem of common vision-based lane recognition systems is their susceptibility to weather. Especially when driving in adverse weather conditions such as rain or snow it is difficult to estimate the road course. The contrast between the white lane markings and the pavement is poor, sometimes the colors of the markings are negated. Furthermore the range of sight is reduced enormously causing a bad prediction of the lane parameters, particularly the curvature. We present a solution which relies not only on finding white markings. Correlating structures parallel to the road over time the horizontal optical flow is calculated. It is then integrated in the lane recognition system estimating the position of the vehicle within the lane and the curvature parameters of the road ahead. The system allows to assign obstacles detected by radar or vision reliably to specific lanes and to drive laterally controlled autonomously even under adverse weather conditions.
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paper_title: Lane detection and tracking using B-Snake

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In this paper, we proposed a B-Snake based lane detection and tracking algorithm without any cameras' parameters. Compared with other lane models, the B-Snake based lane model is able to describe a wider range of lane structures since B-Spline can form any arbitrary shape by a set of control points. The problems of detecting both sides of lane markings (or boundaries) have been merged here as the problem of detecting the mid-line of the lane, by using the knowledge of the perspective parallel lines. Furthermore, a robust algorithm, called CHEVP, is presented for providing a good initial position for the B-Snake. Also, a minimum error method by Minimum Mean Square Error (MMSE) is proposed to determine the control points of the B-Snake model by the overall image forces on two sides of lane. Experimental results show that the proposed method is robust against noise, shadows, and illumination variations in the captured road images. It is also applicable to the marked and the unmarked roads, as well as the dash and the solid paint line roads.
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paper_title: Accurate vision based road tracker

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Vision based road trackers are about to be integrated in current vehicles mainly for security purposes in response to sleepiness problems for example. Nevertheless, such systems must be acceptable for drivers and must have a good reliability both in terms of roadsides recognition and of vehicle location estimation. Such a system must therefore be able to run in spite of difficult situations (due to occlusions, traffic, bad weather conditions, etc). Furthermore, the accuracy of the 3D vehicle estimation must be sufficient in order to feed subsequent warning systems. The system we have designed is able to recognize with reliability the lane sides in the current image and uses a 3D/4D modelling which provides both a good recognition as well as a very accurate 3D parameters estimation (vehicle location, steer angle, road curvatures, etc). The paper focuses mainly on this 3D original estimation stage and presents our recent developments (distance between vehicle and each road side for lane tracking application, analysis distances increasing, vertical road curvature estimation). The algorithm behaviour is then presented in simulated and real situations as well in order to prove the reliability of the approach.
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paper_title: Rear view lane detection by wide angle camera

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In this paper a new lane detection method is proposed for a highway lane departure warning system which hardware cost is reduced by using an existing wide-angle back monitor camera and an embedded CPU on the market. The extended Hough transform and a bird's-eye view image are effectively applied to perform realtime lane detection. The method was built on an embedded CPU (Hitachi SH2E) as software. Then the extended Hough transform was executed at less than 66 milliseconds.
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paper_title: Vision-based vehicle detection and tracking method for forward collision warning in automobiles

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We describe a vision-based vehicle detection and tracking method for forward collision warning in automobiles. The approach is based on a set of edge-based constraint filters that assist in the segmentation of vehicles from background clutter. The detected vehicles are then tracked using a combination of distance based matching, sum-of-square-of-difference in intensity (SSD) and edge density of detected vehicle regions. The computational load for tracking is minimized using a vehicle-clustering algorithm. Experimental results are presented to illustrate the performance of the algorithm.
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paper_title: Obstacle Detection By Vision System For An Autonomous Vehicle

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In this paper, a method to detect an obstacle on unstructured roads using a vision system and a technique for camera calibration is presented. The effectiveness is demonstrated by using a real road image obtained by two cameras mounted on a vehicle.
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paper_title: Vehicle Detection under Day and Night Illumination

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Effective detection of vehicles in urban traffic scenes can be achieved by exploiting image analysis techniques. Nevertheless, vehicle detection in daytime and at night can’t be approached with the same image analysis algorithms, due to the strongly different illumination conditions. This paper describes the two different sets of image analysis algorithms that have been used in the VTTS system (Vehicular Traffic Tracking System) for extracting vehicles from image sequences acquired in daytime and at night. In the system, a supervising level selects the set of algorithms to apply and performs vehicle tracking under control of a rule-based decision module. The paper describes the tracking module, and reports experimental results for both vehicle detection and tracking.
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paper_title: The Use of Optical Flow for Road Navigation

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This paper describes procedures for obtaining a reliable and dense optical flow from image sequences taken by a TV camera mounted on a car moving in usual outdoor scenarios. By using correlation based techniques and by correcting the optical flows for shocks and vibrations, useful sequences of optical flows can be obtained. When the car is moving along a flat road and the optical axis of the TV camera is parallel to the ground, the motion field is expected to be almost quadratic and have a specific structure. As a consequence the egomotion can be estimated from this optical flow and information on the speed and the angular velocity of the moving vehicle are obtained. By analyzing the optical flow it is possible to recover also a coarse segmentation of the flow, in which objects moving with a different speed are identified. By combining information from intensity edges a better localization of motion boundaries are obtained. These results suggest that the optical flow can be successfully used by a vision system for assisting a driver in a vehicle moving in usual streets and motorways.
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paper_title: An image processing system for driver assistance

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In this paper, the authors describe a system designed to extract information from an image acquired from an onboard CCD camera. The purpose of the system is to detect, track and classify objects. An approach that involves integration and fusion in the sequential and parallel phases of sensor and information processing is described. The authors note that the primary advantage of this approach is the integrative coupling of different algorithms providing partly redundant information.
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paper_title: Vehicle detection combining gradient analysis and AdaBoost classification

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This paper presents a real-time vision-based vehicle's rear detection system using gradient based methods and Adaboost classification, for ACC applications. Our detection algorithm consists of two main steps: gradient driven hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, possible target locations are hypothesized. This step uses an adaptive range-dependant threshold and symmetry for gradient maxima localization. Appearance-based hypothesis validation verifies those hypothesis using AdaBoost for classification with illumination independent classifiers. The monocular system was tested under different traffic scenarios (e.g., simply structured highway, complex urban environments, varying lightening conditions), illustrating good performance.
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paper_title: Artificial neural networks in real-time car detection and tracking applications

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In this paper we present three applications representing different approaches to car detection and tracking problems. In conjunction with problem-adequate preprocessing, artificial neural networks are utilized to solve the essential subproblems. The approaches draw their sensory input from vision and times series based cues. All three applications satisfy real-time requirements on German autobahnen.
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paper_title: On-road vehicle detection using Gabor filters and support vector machines

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On-road vehicle detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for rear-view vehicle detection. Specifically, we propose using Gabor filters for vehicle feature extraction and support vector machines (SVM) for vehicle detection. Gabor filters provide a mechanism for obtaining some degree of invariance to intensity due to global illumination, selectivity in scale, and selectivity in orientation. Basically, they are orientation and scale tunable edge and line detectors. Vehicles do contain strong edges and lines at different orientation and scales, thus, the statistics of these features (e.g., mean, standard deviation, and skewness) could be very powerful for vehicle detection. To provide robustness, these statistics are not extracted from the whole image but rather are collected from several subimages obtained by subdividing the original image into subwindows. These features are then used to train a SVM classifier. Extensive experimentation and comparisons using real data, different features (e.g., based on principal components analysis (PCA)), and different classifiers (e.g., neural networks (NN)) demonstrate the superiority of the proposed approach which has achieved an average accuracy of 94.81% on completely novel test images.
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paper_title: Multiple-cue data fusion with particle filters for vehicle detection in night view automotive applications

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In this contribution we present a sensor data fusing concept utilizing particle filters. The investigation aims at the development of a robust and easy to extend approach, capable of combining the information of different sensors. We use the particle filters characteristics and introduce weighting functions that are multiplied during the measurement update stage of the particle filter implementation. The concept is demonstrated in a vehicle detection system that conjoins symmetry detection, tail lamp detection and radar measurements in night vision applications.
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paper_title: Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion

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Active research into vehicle detection and tracking using a vision sensor are done for driver assistance systems (DAS) - collision warning and avoidance, vision enhancement, etc. The vehicle detection and tracking algorithm for DAS requires a robust feature extraction and tracking method regardless of the light and road conditions and an exact estimation of vehicle position and velocity regardless of the distance from the ego-vehicle. But most research was carried out in the day time with good lighting conditions and the little research done so far in the night time assumed no interference of headlights from other vehicles. This paper proposes a new robust vehicle detection and tracking method regardless of the light and road conditions at any distance using vision and sonar sensors. We use the sonar sensor for detection and distance estimation within 10 m and the image sensor over 10 m. First, this paper proposes a simple method that can determine the light condition by observing several images and this light condition is used by selecting one of several detection methods. The proposed vehicle detection method in the day time image can extract the shadow region represented by the boundary between a vehicle and the road and further verify using other vehicle features, such as symmetry rate, vertical edge, and lane information. The vehicle tracking method in the day time uses online template matching using the mean image created by several consecutive detection results. The vehicle detection method in the night time extracts bright regions caused by the headlights, taillights, brake lights, etc. and these candidates are verified by observing several consecutive frames.
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paper_title: Real-time distributed sensor fusion for time-critical sensor readings

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The problem of dynamic distributed sensor fusion, where time critical sensor readings are fused concurrently by physically isolated modules, is considered. A large number of critical applications including target tracking, remote sensing, and autonomous vehicle navigation can depend on interpreting sensor readings in real time. Independent sensor modules receive partially contradictory data, but their answers must agree within computed accuracy bounds. Real-time constraints cause additional uncertainty factors. The dynamic nature of this problem requires that the problem be viewed in four dimensions and processed efficiently. We present a new method that robustly tolerates a limited number of component failures. This method has complexity O(f 4N log N), where N is the total number of modules and f is the number of faulty modules tolerated. Experimental results are given showing the merits of the proposed method.
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paper_title: Predicting Lane Position for Roadway Departure Prevention

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In this paper, the authors focus on predicting lane position for departure prevention. It is shown that the standard method of computing Time to Lane Crossing (TLC) is not necessarily accurate. Two different modeling approaches are then presented for predicting the future lane position of a driver, given current lane position and lateral velocity. The first is a kinematic approach which is used as a baseline, and the second is a memory-based learning approach which is examined for improved performance.
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paper_title: A video-based lane keeping assistant

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We propose a lane keeping assistance system which warns the driver of unintended lane departures. Based on an existing robust video-based lane detection algorithm we compare different methods to detect lane departure. A number of assumptions on driver behaviour in certain situations have been integrated to distinguish between intended and unintended lane departures. We integrated our lane keeping assistant in an experimental car and performed systematic experiments in real traffic situations.
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paper_title: AURORA: a vision-based roadway departure warning system

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AURORA is a vision-based system designed to warn a vehicle driver of possible impending roadway departure accidents. It employs a downward looking color video camera with a wide angle lens, a digitizer, and a portable Sun Sparc workstation. Using a binormalized adjustable template correlation algorithm, it reliably detects lane markers on structured roads at 60 Hz. A time-to-lane-crossing (TLC) measurement is calculated for each image based on the estimation of vehicle's lateral position and velocity. This measurement is used to trigger an alarm when the TLC falls below a preset threshold. Promising results have been achieved under a variety of weather and lighting conditions, on many road types.
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paper_title: Eye glance behavior during lane changes and straight-ahead driving

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Understanding drivers' eye behavior before lane changing is an important aspect of designing usable, safe lane-change collision-avoidance systems (LCAS) that will fit well with drivers' expectations. This understanding could lead to improvements for LCAS as well as for a variety of other collision avoidance systems. Findings regarding driver eye glance behaviors are presented in a comparison of lane change maneuvers with straightahead (baseline) driving events. Specific eye glance patterns before lane change initiation were observed. When preparing to make a lane change to the left as compared with driving straight ahead, drivers doubled the number of glances toward the rearview mirror and were much more likely to look at other locations associated with moving to the left, including the left mirror and blind spot. On the basis of the eye glance patterns observed and previous results, the following recommendations are made: (a) visual presence detection indicator displays should be used to provide informat...
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paper_title: Design of emergency manoeuvres for automated highway system: obstacle avoidance problem

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We analyze the problem of obstacle avoidance in an automated highway system (AHS). For a given scenario (traffic state, obstacle location, etc.), we synthesize the best possible avoidance manoeuvre for each vehicle. Our aim is to obtain a distributed strategy so that the obstacle avoidance manoeuvres can be executed by vehicle based controllers (with some inter-vehicle communication) as opposed to a roadside controller making decisions and communicating it to the individual vehicles.
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paper_title: DRIVER EDUCATION TASK ANALYSIS. VOLUME 1: TASK DESCRIPTIONS

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THIS IS THE FIRST OF A FOUR-VOLUME REPORT DEALING WITH THE DEVELOPMENT OF DRIVER EDUCATION OBJECTIVES THROUGH ANALYSIS OF THE DRIVER'S TASK. INCLUDED ARE A DETAILED DESCRIPTION OF THE VARIOUS TYPES OF BEHAVIOR REQUIRED OF PASSENGER CAR DRIVERS, A RATED EVALUATION OF THESE TYPES OF BEHAVIOR, AND ITEMS OF SUPPORTING INFORMATION RELATING TO DRIVER PERFORMANCE AND PERFORMANCE LIMITS, ENABLING DRIVER KNOWLEDGE, SKILL, AND BEHAVIOR TO BE EVALUATED. THE TASK DESCRIPTIONS HAVE BEEN ORGANIZED IN TERMS OF THE SITUATION, TYPES OF BEHAVIOR INVOLVED IN CONTROLLING THE MOVEMENT OF THE CAR WITHOUT REGARD TO SPECIFIC SITUATIONS, TYPES OF BEHAVIOR OCCURRING CONTINUOUSLY OR PERIODICALLY WHILE DRIVING, RATHER THAN IN RESPONSE TO A SPECIFIC SITUATION, AND TYPES OF BEHAVIOR BEFORE DRIVING, THE AIM OF WHICH IS TO MAINTAIN THE CAR IN SOUND OPERATING CONDITIONS, AND IN COMPLIANCE WITH THE LEGAL REGULATIONS. /RRL/
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paper_title: Collision avoidance analysis for lane changing and merging

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One of the riskiest maneuvers that a driver has to perform in a conventional highway system is to merge into the traffic and/or to perform a lane changing maneuver. Lane changing/merging collisions are responsible for one-tenth of all crash-caused traffic delays often resulting in congestion. Traffic delays and congestion, in general, increase travel time and have a negative economic impart. We analyze the kinematics of the vehicles involved in a lane changing/merging maneuver, and study the conditions under which lane changing/merging crashes can be avoided. That is, given a particular lane change/merge scenario, we calculate the minimum longitudinal spacing which the vehicles involved should initially have so that no collision, of any type, takes place during the maneuver. Simulations of a number of examples of lane changing maneuvers are used to demonstrate the results. The results of this paper could be used to assess the safety of lane changing maneuvers and provide warnings or take evasive actions to avoid collision when combined with appropriate hardware on board of vehicles.
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Title: A Review of Lateral Driver Support Systems

Section 1: Introduction
Description 1: Introduce the concept of lateral driver support systems, including the main function and sub-functions (Sense, Think, Act).

Section 2: Sense
Description 2: Discuss how sensor technology is used to capture information about the traffic environment surrounding the vehicle.

Section 3: Think
Description 3: Describe how safety assessment algorithms interpret the information captured by sensors and decide on the actions to be executed.

Section 4: Act
Description 4: Explain how actions are executed through human-machine interfaces and vehicle control devices.

Section 5: Sensor Technology in Lateral Driver Support Systems
Description 5: Analyze the different sensors (active and passive) used in lateral driver support systems and their respective advantages and disadvantages.

Section 6: Vision Based Lane Detection
Description 6: Outline methods for vision-based lane detection, classifying them into feature-based and model-based methods.

Section 7: Vision Based Vehicle Detection
Description 7: Provide an in-depth look into the methods for vision-based vehicle detection, including hypothesis generation (HG) and hypothesis verification (HV).

Section 8: Vehicle Detection Based on Sensor Fusion
Description 8: Present methods for vehicle detection using sensor fusion, combining information from various sensors.

Section 9: Algorithms for Lane Departure Warning
Description 9: Discuss safety assessment algorithms used in lane departure warning systems, based on vehicle position, time to line crossing, and interpretation of the road scene.

Section 10: Algorithms for Lane Change Assistance
Description 10: Examine the algorithms for lane change assistance, distinguishing between autonomous and non-autonomous support systems.

Section 11: Conclusion
Description 11: Summarize the findings of the review, including the effectiveness of sensor technology, detection algorithms, and safety assessment algorithms in reducing lateral accidents.