Early Detection of Hazards in Driving Situations through Multi-Sensor Fusion

This article proposes a novel approach for detecting hazardous events in driving situations. The assessment of hazards is based on the detection of atypical situations. The main assumption is that driving situations might get dangerous when an implicit normal state is not given any more. A prototype for the detection of atypical driving situations has been developed. In a first step, a multi-sensor multi-level fusion framework is presented and exemplified by object detection based on a camera and a laser scanner. The detected objects with their specific behaviours are transformed into a Statistical Information Grid (SIG), which is filled up with training information. In the working phase, current situations are compared to the statistical information and declared as atypical if under a given threshold. The prototype has been implemented. It has been shown that the recognition of atypical driving events is possible in selected driving situations

Self-Calibration System for the Orientation of a Vehicle Camera

The process of calibration is a prerequisite for each computer vision system. Calibration involves calculating both intrinsic and extrinsic parameters of the camera. While intrinsic parameters (focal length, principal point, etc.) are usually fixed, extrinsic ones (position and angles of the camera) have to be determined when the camera moves in relation to world coordinates. The calibration of the extrinsic parameters is usually performed with help of some reference objects or known measured points (GCP’s) in the scene. In the case of a vehicle camera, where the coordinates refer to vehicle coordinates, not the extrinsic calibration but the alignment of the camera to the Inertial Measurement Unit (IMU) is necessary. This paper proposes a solution for determining the orientation of a vehicle camera in relation to the vehicle. This novel approach escapes from tedious laboratory setups and reference measurements. It benefits from a known property of the road’s infrastructure, namely the parallelism of the road markers. For this reason lane markers are detected and transformed through a fast perspective removal (FPR) to an orthographic perspective. Newton’s Method is used for searching an optimal parameter set for this transformation. The algorithm works under the assumptions that the calibration is performed when driving on a straight and flat segment and the lane markers are visible. It reaches very good performance (via parametrical instead of image transformations) and good accuracy for lateral detection of features in automotive applications (for depth information, the algorithm must be improved)

Image and Laser Scanner Processing as Confident Cues for Object Detection in Driving Situations

In the area of advanced driver assistance and automation systems knowledge about the vehicle environment is becoming more and more important in order to increase traffic safety. This paper is concerned with the detection and tracking of objects in the proximity of the ego-vehicle while driving on highways. For this purpose, a camera sensor and a laser scanner are used. The processed data of the sensors is then fused at object level in a competitive way. The paper focuses on the generation of object observations by applying the mentioned sensors. In the case of the camera system, an image processing method based on texture information is presented. The texture information is adaptively calculated in order to be independent of the lighting conditions. Taking into account knowledge about the image structure in driving situations, texture segments are classified and object observations are generated. In comparison to other methods, objects are detected independently of any features, model and movement assumptions. For object generation from laser scanner data, a method characterizing detected object contours by means of a shape indicator (long, corner, round, concave etc.) is proposed. Different to other works in this field, in the method presented here explicitly obtains the objects’ optimal reference point and the observability of the objects’ components. The experiments conducted both with simulated and real data show the plausibility of the methods to be used as cues for an object fusion system