Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

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On-road vehicle detection: a review

GPS satellite (4) ranging signals (6) received (32) on comm1, and DGPS auxiliary range correction signals and pseudolite carrier phase ambiguity resolution signals (8) from a fixed known earth base station (10) received (34) on comm2, at one of a plurality of vehicles/aircraft/automobiles (2) are computer processed (36) to continuously determine the one's kinematic tracking position on a pathway (14) with centimeter accuracy. That GPS-based position is communicated with selected other status information to each other one of the plurality of vehicles (2), to the one station (10), and/or to one of a plurality of control centers (16), and the one vehicle receives therefrom each of the others' status information and kinematic tracking position. Objects (22) are detected from all directions (300) by multiple supplemental mechanisms, e.g., video (54), radar/lidar (56), laser and optical scanners. Data and information are computer processed and analyzed (50,52,200,452) in neural networks (132, FIGS. 6-8) in the one vehicle to identify, rank, and evaluate collision hazards/objects, an expert operating response to which is determined in a fuzzy logic associative memory (484) which generates control signals which actuate a plurality of control systems of the one vehicle in a coordinated manner to maneuver it laterally and longitudinally to avoid each collision hazard, or, for motor vehicles, when a collision is unavoidable, to minimize injury or damage therefrom. The operator is warned by a heads up display and other modes and may override. An automotive auto-pilot mode is provided.

GPS vehicle collision avoidance warning and control system and method

A forward-facing vision system for a vehicle includes a forward-facing camera disposed in a windshield electronics module attached at a windshield of the vehicle and viewing through the windshield. A control includes a processor that, responsive to processing of captured image data, detects taillights of leading vehicles during nighttime conditions and, responsive to processing of captured image data, detects lane markers on a road being traveled by the vehicle. The control, responsive to lane marker detection and a determination that the vehicle is drifting out of a traffic lane, may control a steering system of the vehicle to mitigate such drifting, with the steering system manually controllable by a driver of the vehicle irrespective of control by the control. The processor, based at least in part on detection of lane markers via processing of captured image data, determines curvature of the road being traveled by the vehicle.

Vision system for vehicle

System and method for obtaining information about occupancy of a compartment in a movable object in which at least first and second optical imagers obtain images of a common area of the compartment and spaced apart from one another. Processing circuitry derives information from the images obtained by the imagers. A light source may illuminate the common area of the compartment and be interposed between the imagers. The processing circuitry can include a microprocessor with at least one pattern recognition algorithm and be arranged to determine the distance between the imagers and an object in the common area by locating a specific feature in the common area by first locating the feature in only the image obtained by one imager, then determining the location of the same feature in the image obtained by another imager, and determining the distance of the feature from the imagers by triangulation.

Vehicular restraint system control system and method using multiple optical imagers

A vehicular vision system includes a camera having a lens and a CMOS photosensor array having a plurality of photosensor elements. The camera is disposed at an interior portion of a vehicle equipped with the vehicular vision system. The camera one of (i) views exterior of the equipped vehicle through the windshield of the equipped vehicle and forward of the equipped vehicle and (ii) views from the windshield of the equipped vehicle into the interior cabin of the equipped vehicle. A control includes an image processor that processes image data captured by the photosensor array. The image processor processes captured image data to detect an object viewed by the camera. The photosensor array is operable at a plurality of exposure periods and at least one exposure period of the plurality of exposure periods is dynamically variable.

Vehicular vision system

RCAS (rear-end collision avoidance system) has been developed as a system of ASV (advanced safety vehicle). It is a driver assist system to avoid the rear-end collision by informing the distance headway. It also has automatic braking function in case of emergency and distance warning function to the trailing vehicle. This paper describes the outline of RCAS, techniques about the preceding vehicle recognition and collision potential hazard evaluation. And experimental results about distance headway warning and collision avoidance by automatic braking are reported.

Development of rear-end collision avoidance system

Sensor fusion method is more robust than the method using a single sensor, so sensor fusion is effective for vehicle recognition in a complex scene. However, if each sensing data is processed individually for most of the stages, recognition performance is not always good. In this paper, we propose the extensible and generalized fusion method. First, fusion vector which is combined with image sensor data and laser radar data at a primitive level is prepared. We regard fusion vector as sensing data by one robust sensor. Next, fusion vector is compared with a discriminated dictionary. We report the efficiency of our method at a complex scene in which recognition error tends to occur by using a single sensor.

Vehicles detection using sensor fusion

The purpose of this study is to develop an algorithm for the detection of the obstacle ahead of a host vehicle by image processing using a monocular onboard camera, and to show that this algorithm is able to apply the prevention of the collision accident. It can be supposed that there is some important information on the edge of the image. In this paper we show an obstacle detection method by extracting optical flow along the edge of the image, and estimated result of the three dimensional (3D) information in the world coordinate system from the optical flow.

The Obstacle Detection Method using Optical Flow Estimation at the Edge Image

In the rear-end collision avoidance system (RCAS), which incorporates the functions of detecting and reporting distance headway as well as of collision avoidance through the use of automatic braking, the development of the technique to integrate the advanced characteristic of each sensors becomes important. In this paper, we propose the uniting processing technique which uses the network as a trial. We classify the recognition system into some modules from the software point of view, link these modules as the network and make the states of the network correspond to the driving environment. We referred to a immune network when making the network. The immune network is a technological model of the immune mechanism in the living body. We applied this network mechanism to fuse functions of the distance headway, the road lane position and the position of the preceding vehicle obtained by the laser radar and image processing. These three functions are transferred to modules, each module tests mutually, and each certainty degree is changed continuously. This paper describes this network technique and the experimental results.

Understanding driving situations using a network model

PROBLEM TO BE SOLVED: To provide a technology for discriminating and highly accurately recognizing a vehicle, motorbike, and person (pedestrian) or the like based on the detection pattern of the reflection of an in-vehicle radar even when a distance between the vehicle and an object as the target of recognition changes. SOLUTION: The detection pattern of the reflection of a laser radar 2 which changes according to a distance between an object as the target of search and the laser radar 2 is stored in a storage part 7 as the characteristic pattern of each distance for each type of the object, and the detection pattern of the reflection of the laser radar 2 is collated with the characteristic pattern stored in the storage part 7 by a recognition processing part 4, and the type of the object of the detection pattern is discriminated, so that even when the distance between the vehicle and the object as the target of recognition changes, it is possible to discriminate and highly accurately recognize the vehicle, motorbike, bicycle and pedestrian (person) or the like based on the detection pattern of the reflection of the laser radar 2. COPYRIGHT: (C)2011,JPO&INPIT

Toshio Ito

Papers

Development of rear-end collision avoidance system

Vehicles detection using sensor fusion

The Obstacle Detection Method using Optical Flow Estimation at the Edge Image

Understanding driving situations using a network model

Object recognition apparatus