Preference and placement of vehicle crash sensors

Senzori za detekciju sudara su od bitne važnosti u aplikacijama za sigurnost vozila. Jedna od najvažnijih primjena tih mjernih sustava je u sustavima za vezanje vozača u vozilu. Uz to, senzori se uveliko koriste za ublažavanje udesa i u razvijenom sustavu upravljanja vozilom. Senzori za otkrivanje sudara su se u zadnjih nekoliko desetljeća značajno razvili. Ipak, postojeći zahtjevi i izazovi dovode do novih inovacija i poboljšanja njihovih funkcija. U ovom se radu daje pregled senzorskih tehnologija i postavljanja senzora za otkrivanje udesa s naglaskom na otkrivanje sudara prevrtanjem vozila. Daje se i prijedlog za izbor senzora za otkrivanje pojedinog sudara ovisno o radnim karakteristikama senzora. Potražnja za odgovarajućim senzorima u sigurnom sustavu vozila ostaje izazov i aktivno područje rada u nadolazećim godinama. Zato ovaj pregled treba koristiti kao putokaz istraživačima koji se žele baviti detekcijom sudara i izborom senzora.Crash detection sensors play a vital role in vehicular safety applications. One of the major applications of these sensing systems is the use in occupant restraint systems. Besides, sensors are extensively used in the accident mitigation and advanced vehicle control system. Crash sensors have advanced significantly in the last few decades. Yet, existing demands and challenges bring new innovations and improvements in their functions. This paper reviews the sensor technologies and placement of sensors for accident detections with an emphasis on the rollover crash detection. The paper also suggests sensor selection for particular crash detection depending on the performance of the sensors. The demand for the sensors for a responsive driving environment and safe vehicle system shall remain a challenging and active area for years to come. Thus, this review shall work as a guideline for the researchers who wish to study on the crash detection and sensor selection

Studies on Sensor Aided Positioning and Context Awareness

This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context