Experimental System to Support Real-Time Driving Pattern Recognition

Lecce, Vincenzo Di; Calabrese, Marco

doi:10.1007/978-3-540-85984-0_143

Cited by 15 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy logic extended to deal with the concept of partial truth, where the truth value is ranging between completely true and completely false. Fuzzy logic inference is a simple approach to solving problems instead of attempting to model it mathematically, which results in the FIS depending on human experience more than the technical understanding of the problem (Lecce and Calabrese, 2008). The fuzzy inference system consists of three stages: fuzzification, fuzzy inference and defuzzification to classify the different risk levels of each manoeuvre.…”

Section: Irregular Driving Detectionmentioning

confidence: 99%

“…For real-time driving pattern detection, various sensors, such as positioning, orientation, velocity and vision, are used to detect vehicle motion information, and the collected data are subsequently analysed to find cues of irregular driving. Lecce and Calabrese (2008) proposed a system based on position and acceleration collection from the Global Positioning System (GPS) and accelerator, and used pattern matching to identify and classify driving styles. Chang et al (2008) developed a vision-based system with the function of learning the trajectories and longitudinal and lateral velocities of the vehicle and then used fuzzy neural network-based image processing information to identify the danger level of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Algorithm for Lane Level Irregular Driving Identification

et al. 2015

View full text Add to dashboard Cite

Global Navigation Satellite Systems (GNSS) are used widely in the provision of Intelligent Transportation System (ITS) services. Today, there is an increasing demand on GNSS to support applications at lane level. These applications required at lane level include lane control, collision avoidance and intelligent speed assistance. In lane control, detecting irregu-lar driving behaviour within the lane is a basic requirement for safety related lane level appli-cations. There are two major issues involved in lane level irregular driving identification: access to high accuracy positioning and vehicle dynamic parameters, and extraction of erratic driving behaviour from this and other related information. This paper proposes an inte-grated algorithm for lane level irregular driving identification. Access to high accuracy posi-tioning is enabled by GNSS and its integration with an Inertial Navigation System (INS) using filtering with precise vehicle motion models and lane information. The identification of irregu-lar driving behaviour is achieved by algorithms developed for different types of events based on the application of a Fuzzy Inference System (FIS). The results show that decimetre level accuracy can be achieved and that different types of lane level irregular driving behaviour can be identified.

show abstract

Section: Irregular Driving Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Lane Level Irregular Driving Identification

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In [17], Leece et al have proposed an architecture for driving information system with specific sensors and GPS receiver. They have collected the acceleration and GPS data and used pattern matching to identify and classify driving styles.…”

Section: Related Workmentioning

confidence: 99%

Mobile phone based drunk driving detection

Dai¹,

Teng²,

Bai³

et al. 2010

Proceedings of the 4th International ICST Conference on Pervasive Computing Technologies for Healthcare

278

153

View full text Add to dashboard Cite

Drunk driving, or officially Driving Under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. A program installed on the mobile phone computes accelerations based on sensor readings, and compares them with typical drunk driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens. We implement the detection system on Android G1 phone and have it tested with different kinds of driving behaviors. The results show that the system achieves high accuracy and energy efficiency.

show abstract

“…The underpinning idea is to extract anomalous driving cues from information obtained from the vehicle's motion sensors, including position, orientation, and velocity, to classify different dangerous driving styles and provide warning messages with recommended actions. Lecce et al (2008) develop a driving information collection system based on a specific senor and GPS receiver, and apply pattern matching for the classification of driving styles. Their study is preliminary and does not present simulation or field test results.…”

Section: Introductionmentioning

confidence: 99%