Vehicle recognition and tracking using a generic multi-sensor and multi-algorithm fusion approach

International audienceThis paper tackles the problem of improving the robustness of vehicle detection for Adaptive Cruise Control (ACC) applications. Our approach is based on a multisensor and a multialgorithms data fusion for vehicle detection and recognition. Our architecture combines two sensors: a frontal camera and a laser scanner. The improvement of the robustness stems from two aspects. First, we addressed the vision-based detection by developing an original approach based on fine gradient analysis, enhanced with a genetic AdaBoost-based algorithm for vehicle recognition. Then, we use the theory of evidence as a fusion framework to combine confidence levels delivered by the algorithms in order to improve the classification 'vehicle versus non-vehicle'. The final architecture of the system is very modular, generic and flexible in that it could be used for other detection applications or using other sensors or algorithms providing the same outputs. The system was successfully implemented on a prototype vehicle and was evaluated under real conditions and over various multisensor databases and various test scenarios, illustrating very good performances

Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

Enhancing Traffic Safety in Unpredicted Environments with Integration of ADAS Features with Sensor Fusion in Intelligent Electric Vehicle Platform with Implementation of Environmental Mapping Technology

A major objective on society is to reduce the number of accidents and fatalities on the road for drivers, and pedestrians. Therefore, the automotive engineering field is working on this problem through the development and integration of safety technologies such as advanced driving assistance systems. For this reason, this work was intended to develop and evaluate the performance of different ADAS features and IV technologies under unexpected scenarios. This by the development of safety algorithms applied to the intelligent electric vehicle designed and built in this work, through the use of ADAS sensors based on sensor fusion. Evaluation of AEB, PA, steering by wire, and machine learning based distance predictions, has been studied in this work bringing a contribution to driver safety and the well-being of pedestrians. Based on this work, the enhancement of distance precision of ADAS features with a percentage error of 3.89% compared to average of raw sensors data was found as well as an study of impact of color in LiDAR data quality

Investigation of user needs for driver assistance

Advanced Driver Assistance Systems (ADAS) aim at supporting the driver with the driving task and are expected to lead to a safer, cleaner and more efficient and comfortable transport system. This paper presents the results of an Internet questionnaire among more than 1000 Dutch car drivers. Respondents indicated their needs for driver assistance with certain driving tasks and situations. It appeared that warnings for downstream traffic conditions and traffic in blind spots were favoured. Apparently, the needs for warnings for downstream traffic conditions on motorways significantly differed from the needs for other driver support functions. Moreover, drivers preferred the ideal system to help them with critical situations (i.e. imminent crash and reduced visibility) and car following on motorways to other driving tasks and situations. Characteristics of the driver, system and traffic scene affected the needs for driver support. Besides, these needs indicated consequences for the integration of driver assistance. Driver support functions should exchange information to extend their individual fields of activity, for example by inter-vehicle communication (e.g. warning for downstream traffic conditions) or sensor data fusion (e.g. warning for an imminent crash)

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

Road Infrastructure Challenges Faced by Automated Driving: A Review

Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS

Comprehensive concept-phase system safety analysis for hybrid-electric vehicles utilizing automated driving functions

2019 Summer.Includes bibliographical references.Automotive system safety (SS) analysis involving automated driving functions (ADFs) and advanced driver assistance systems (ADAS) is an active subject of research but highly proprietary. A comprehensive SS analysis and a risk informed safety case (RISC) is required for all complex hybrid-vehicle builds especially when utilizing ADFs and ADAS. Industry standard SS procedures have been developed and are accessible but contain few detailed instructions or references for the process of completing a thorough automotive SS analysis. In this work, a comprehensive SS analysis is performed on an SAE-Level 2 autonomous hybrid-vehicle architecture in the concept phase which utilizes lateral and longitudinal automated corrective control actions. This paper first outlines a proposed SS process including a cross-functional SS working group procedure, followed by the development of an item definition inclusive of the ADFs and ADAS and an examination of 5 hazard analysis and risk assessment (HARA) techniques common to the automotive industry that were applied to 11 vehicle systems, and finally elicits the safety goals and functional requirements necessary for safe vehicle operation. The results detail functional failures, causes, effects, prevention, and mitigation methods as well as the utility of, and instruction for completing the various HARA techniques. The conclusion shows the resulting critical safety concerns for an SAE Level-2 autonomous system can be reduced through the use of the developed list of 116 safety goals and 950 functional safety requirements