Martin Fritzsche

Drivers’ misjudgement of vigilance state during prolonged monotonous daytime driving

To investigate the effects of monotonous daytime driving on vigilance state and particularly the ability to judge this state, a real road driving study was conducted. To objectively assess vigilance state, performance (auditory reaction time) and physiological measures (EEG: alpha spindle rate, P3 amplitude; ECG: heart rate) were recorded continuously. Drivers judged sleepiness, attention to the driving task and monotony retrospectively every 20 min. Results showed that prolonged daytime driving under monotonous conditions leads to a continuous reduction in vigilance. Towards the end of the drive, drivers reported a subjectively improved vigilance state, which was contrary to the continued decrease in vigilance as indicated by all performance and physiological measures. These findings indicate a lack of self-assessment abilities after approximately 3h of continuous monotonous daytime driving.

EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions

OBJECTIVE The purpose of this study is to show the effectiveness of EEG alpha spindles, defined by short narrowband bursts in the alpha band, as an objective measure for assessing driver fatigue under real driving conditions. METHODS An algorithm for the identification of alpha spindles is described. The performance of the algorithm is tested based on simulated data. The method is applied to real data recorded under real traffic conditions and compared with the performance of traditional EEG fatigue measures, i.e. alpha-band power. As a highly valid fatigue reference, the last 20 min of driving from participants who aborted the drive due to heavy fatigue were used in contrast to the initial 20 min of driving. RESULTS Statistical analysis revealed significant increases from the first to the last driving section of several alpha spindle parameters and among all traditional EEG frequency bands, only of alpha-band power; with larger effect sizes for the alpha spindle based measures. An increased level of fatigue over the same time periods for drop-outs, as compared to participants who did not abort the drive, was observed only by means of alpha spindle parameters. CONCLUSIONS EEG alpha spindle parameters increase both fatigue detection sensitivity and specificity as compared to EEG alpha-band power. SIGNIFICANCE It is demonstrated that alpha spindles are superior to EEG band power measures for assessing driver fatigue under real traffic conditions.

Multi-sensor self-localization based on Maximally Stable Extremal Regions

This contribution presents a precise localization method for advanced driver assistance systems. A Maximally Stable Extremal Region (MSER) detector is used to extract bright areas, i.e. lane markings, from grayscale camera images. Furthermore, this algorithm is also used to extract features from a laser scanner grid map. These regions are automatically stored as landmarks in a geospatial data base during a map creation phase. A particle filter is then employed to perform the pose estimation. For the weight update of the filter the similarity between the set of online MSER detections and the set of mapped landmarks within the field of view is evaluated. Hereby, a two stage sensor fusion is carried out. First, in order to have a large field of view available, not only a forward facing camera but also a rearward facing camera is used and the detections from both sensors are fused. Secondly, the weight update also integrates the features detected from the laser grid map, which is created using measurements of three laser scanners. The performance of the proposed algorithm is evaluated on a 7 km long stretch of a rural road. The evaluation reveals that a relatively good position estimation and a very accurate orientation estimation (0.01 deg ± 0.22 deg) can be achieved using the presented localization method. In addition, an evaluation of the localization performance based only on each of the respective kinds of MSER features is provided in this contribution and compared to the combined approach.

High-performance on-road vehicle detection in monocular images

This paper addresses the problem of monocular vehicle detection for forward collision warning. We present a system that is able to process large images with high speed and delivers high detection rates at only one false alarm every 100 frames.

Precise timestamping and temporal synchronization in multi-sensor fusion

This paper presents a new approach to exact timestamping of asynchronous measurements in a multi-sensor setup. In order to improve the performance of a single sensor, driver assistance systems use several different sensors which have different latencies that usually cannot be measured directly. These unknown latencies pose a problem in data association and temporal synchronization. Consequently, a method to estimate or incorporate the latencies is needed in all sensor fusion algorithms in order to derive the real time of a measurement. In this paper a method is described to compensate the sensor latencies even if they cannot be observed directly.

A learning concept for behavior prediction in traffic situations

Future driving assistance systems will need an increase ability to handle complex driving situations and to react appropriately according to situation criticality and requirements for risk minimization. Humans, driving on motorways, are able to judge, for example, cut-in situations of vehicles because of their experiences. The idea presented in this paper is to adapt these human abilities to technical systems and learn different situations over time. Case-Based Reasoning is applied to predict the behavior of road participants because it incorporates a learning aspect, based on knowledge acquired from the driving history. This concept facilitates recognition by matching actual driving situations against stored ones. In the first instance, the concept is evaluated on action prediction of vehicles on adjacent lanes on motorways and focuses on the aspect of vehicles cutting into the lane of the host vehicle.

Localization based on region descriptors in grid maps

This paper presents a novel approach towards highly precise self-localization of a vehicle on a digital map. The proposed approach utilizes a map containing region descriptors extracted from ordinary occupancy grid maps. The Maximally Stable Extremal Regions (MSER) algorithm provides robust feature extraction from grid maps in a completely unsupervised process. This allows for the automatic creation of huge maps. Since only single region descriptor points of grid maps are saved in the map database, the data volume of the produced map is kept low. The approach uses a particle filter to estimate the vehicle position on the digital map. The particle filter associates MSER features extracted from an online generated grid map with features of the digital map. An evaluation with real world sensor data, collected on a German rural road, shows that the approach locates the vehicle very precisely.

Impact of out-of-sequence measurements on the joint integrated probabilistic data association filter for vehicle safety systems

This paper addresses the problem of joint state and existence estimation in the presence of temporally asynchronous measurements. In multi-sensor fusion, the problem can occur that measurements from different sensors can arrive at the processing unit out of sequence, i.e., the original temporal order of the measurements is lost. For the first time, the influence of these out-of-sequence measurements on state estimation as well as on existence estimation is examined. The existence probabilities are estimated via the Joint Integrated Probabilistic Data Association Filter (JIPDA) [17]. Two different methods to deal with out-of-sequence measurements in JIPDA are described and compared. It is shown that the handling of out-of-sequence measurements has a considerable influence not only on state, but also on existence estimation.

Temporal synchronization in multi-sensor fusion for future driver assistance systems

A new approach to precise timestamping and temporal synchronization in a multi-sensor setup is presented. Modern driver assistance systems use an increasing amount of different sensors that do not provide the exact timestamps of the measurements, nor the period of time between two measurements. This paper describes a method to determine these timestamps up to millisecond accuracy. Possible drifts of the internal sensor clocks are taken into account. In addition, a frequent problem are lost measurements that lead to large errors in the timestamping. These framedrops are detected reliably and handled adequately via the method at hand. Finally a method to evaluate the performance of the whole timestamping procedure is described and real-world results are presented.

Detection of buried land mines using ground-penetrating radar

Ground Penetrating Radar (GPR) has become widely accepted as a major technique for subsoil investigations over the recent years, mainly in civil engineering. Another field of application, on a global scale, is the pollution of vast areas with land mines, especially in countries of former armed conflicts. According to UN estimates, the number of buried anti-personnel mines exceeds 100 million, with 15,000 people killed every year. The rate of new mines being layed is about one million per year and surpasses the number of mines cleared by a factor of twenty. This demonstrates the need to develop new technologies to increase the efficiency of mine clearing operations. The intension of this paper is to give a short review of the underlying principles and limitations of the GPR-technique. The advantage of 3D versus 2D image processing techniques to enhance data quality and thus detection probability is demostrated, using measured data from sandbox experiments with buried plastic mines. The processed data presented show vertical and horizontal planes through the subsurface and give a clear indication of the buried objects. Factors determining the resolution of the method are discussed. Measurements taken from stones are compared with data obtained from buried mines. The mine data exhibit specific resonances, which is probably due to a minor metal content.