Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems

World-wide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error. The main objective of this research is to develop a predictive system for driving maneuvers by analyzing the cognitive behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle is being driven). Advanced Driving Assistance Systems (ADAS) include different driving functions, such as vehicle parking, lane departure warning, blind spot detection, and so on. While much research has been performed on developing automated co-driver systems, little attention has been paid to the fact that the driver plays an important role in driving events. Therefore, it is crucial to monitor events and factors that directly concern the driver. As a goal, we perform a quantitative and qualitative analysis of driver behavior to find its relationship with driver intentionality and driving-related actions. We have designed and developed an instrumented vehicle (RoadLAB) that is able to record several synchronized streams of data, including the surrounding environment of the driver, vehicle functions and driver cephalo-ocular behavior, such as gaze/head information. We subsequently analyze and study the behavior of several drivers to find out if there is a meaningful relation between driver behavior and the next driving maneuver

Accelerated artificial neural networks on FPGA for fault detection in automotive systems

Modern vehicles are complex distributed systems with critical real-time electronic controls that have progressively replaced their mechanical/hydraulic counterparts, for performance and cost benefits. The harsh and varying vehicular environment can induce multiple errors in the computational/communication path, with temporary or permanent effects, thus demanding the use of fault-tolerant schemes. Constraints in location, weight, and cost prevent the use of physical redundancy for critical systems in many cases, such as within an internal combustion engine. Alternatively, algorithmic techniques like artificial neural networks (ANNs) can be used to detect errors and apply corrective measures in computation. Though adaptability of ANNs presents advantages for fault-detection and fault-tolerance measures for critical sensors, implementation on automotive grade processors may not serve required hard deadlines and accuracy simultaneously. In this work, we present an ANN-based fault-tolerance system based on hybrid FPGAs and evaluate it using a diesel engine case study. We show that the hybrid platform outperforms an optimised software implementation on an automotive grade ARM Cortex M4 processor in terms of latency and power consumption, also providing better consolidation

Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe

In order to build better human-friendly human-computer interfaces, such interfaces need to be enabled with capabilities to perceive the user, his location, identity, activities and in particular his interaction with others and the machine. Only with these perception capabilities can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the development of novel techniques for the visual perception of humans and their activities, in order to facilitate perceptive multimodal interfaces, humanoid robots and smart environments. My work includes research on person tracking, person identication, recognition of pointing gestures, estimation of head orientation and focus of attention, as well as audio-visual scene and activity analysis. Application areas are humanfriendly humanoid robots, smart environments, content-based image and video analysis, as well as safety- and security-related applications. This article gives a brief overview of my ongoing research activities in these areas

Columbia's first flight shakes down space transportation system

The first space shuttle mission is described. Topics include launch preparations, flight profile, trajectory, and landing operations. The spaceflight tracking and data network is discussed and the photography and television schedules are included

Comparison of VLP-16 and MRS-1000 LiDAR systems with absolute interferometer

Nowadays, LiDARs hold a relevant place in providing the environmental sensing required by most ADAS. Promoted by such increasing demand, many new manufacturers are emerging and, new LiDARs are continuously made available on the market. If, on the one hand, the availability of LiDARs with increasing performance and reducing cost has brought significant benefits also promoting the spread of such measuring systems in other areas such as industrial controls and agriculture, on the other, it has made it more difficult to extricate in the immense set of LiDARs present on the market today. In response to this growing need for standards and methods capable of comparing the various LiDARs, many international standards and scientific publications are being produced on the subject. In this paper, we continue our work on LiDARs characterization, focusing our attention on comparing the performances of two of the must popular systems - namely, the MRS 1000 by Sick and the VLP 16 by Velodyne. Starting from the analysis of the warm-up time and stability, such a comparison focused on analyzing the axial error of both systems. Such errors have been estimated by exploiting a custom rail system and an absolute interferometer. The obtained results revealed warm-up times of a few tens of minutes and maximum absolute axial errors of a few centimeters in the range [1.5,21] m