scholarly journals Driver Cognitive Distraction Detection Using Driving Performance Measures

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Lisheng Jin ◽  
Qingning Niu ◽  
Haijing Hou ◽  
Huacai Xian ◽  
Yali Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Measures ◽  
Traffic Accidents ◽  
Can Bus ◽  
Driving Performance ◽  
Cognitive State ◽  
Area Network ◽  
Characteristic Parameters ◽  
Secondary Tasks ◽  
Cognitive Distraction

Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state.

Data Compression and Transmission Method of Vehicle Monitoring Information Collection Based on CAN Bus

2015 ◽  
Vol 7 (2) ◽  
pp. 20-29
Author(s):  
Yingji Liu ◽  
Kan Zhao ◽  
Chen Ding ◽  
Yu Yao
Keyword(s):  
Data Acquisition ◽  
Data Compression ◽  
Real Time ◽  
Remote Monitoring ◽  
Data Transmission ◽  
Can Bus ◽  
Area Network ◽  
Transmission Method ◽  
Remote Monitoring System ◽  
Transmission Cost

Real-time remote monitoring and fault diagnosis for commercial buses has important significance in reducing the occurrence of potential accidents. This paper presents a real-time remote monitoring system for the running state of commercial passenger buses. The vehicle Controller Area Network (CAN) bus is able to collect the information of key indicators being monitored, such as brake pressure, oil pressure and fault code. Then, the collected data are uploaded to the central remote monitoring platform through a General Packet Radio Service (GPRS) module for further analysis and decision-making. In this work, a classification based data acquisition method and a hybrid configuration data transmission method are proposed to improve the efficiency of data acquisition and transmission. The authors also proposed a Run-length based relative coding algorithm to compress the massive monitoring data. Experimental results shows the average data compression ratio is 32.17%, which effectively reduces the data transmission cost.

Development of Field Control System of Automated Guided Vehicle Based on Wireless Local Area Network and CAN Bus

2013 ◽  
Vol 579-580 ◽  
pp. 792-797
Author(s):  
Yan Wang ◽  
Zhong Da Yu ◽  
Chen Xing Bao ◽  
Dong Xiang Shao
Keyword(s):  
Control System ◽  
Real Time ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Can Bus ◽  
Control Unit ◽  
Local Area ◽  
Automated Guided Vehicle ◽  
Area Network ◽  
Control Units

In this paper, we realize a real-time communication based on wireless local area network (WIFI) and controller area network (CAN) bus and develop a distributed control system for an automated guided vehicle (AGV). The system consists of two levels: (1) communication between AGVs and main computer based on WIFI, (2) communicationg between control units of AGV based on CAN bus. A real-time operating system μC/OS-II was used to control time, which significantly reduces the time for program and improves development efficiency. Finally, a small-size distributed AGV controller is developed as the main control unit of AGV and a distributed I/O system is developed based on it.

scholarly journals Effect of Cognitive Distraction on Physiological Measures and Driving Performance in Traditional and Mixed Traffic Environments

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Qiang Hua ◽  
Lisheng Jin ◽  
Yuying Jiang ◽  
Baicang Guo ◽  
Xianyi Xie
Keyword(s):  
Repeated Measures ◽  
Traffic Accidents ◽  
Rapid Development ◽  
Driving Performance ◽  
Intelligent Vehicles ◽  
Distracted Driving ◽  
Steering Wheel ◽  
Physiological Measures ◽  
Mixed Traffic ◽  
Cognitive Distraction

Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents.

Coordinating Cognitive Assistances With PID-Based Control Approaches

2010 ◽  
Author(s):  
Hua Cai ◽  
Yingzi Lin ◽  
Jeffrey Breugelmans
Keyword(s):  
Real Time ◽  
Cognitive Processing ◽  
Interface Problem ◽  
Cognitive State ◽  
Eye Tracker ◽  
Real Time Control ◽  
Secondary Tasks ◽  
Control Approach ◽  
Time Control ◽  
Cognitive Assistance

In human-machine cooperation, machines may assist operators in a variety of ways. This paper discusses the coordination of various assistances on cognitive basis through a PID-based control approach. Cognitive assistance can be viewed as a two-dimensional problem. The question of when to provide assistance can be viewed as a control problem, and the question of what assistance to provide can be viewed as an interface problem. This research proposes pairing cognitive engagement level and performance relevant situation criticality with a PID control approach to determine the appropriate moment to provide proper assistance. Based on the stage of human cognitive processing, the interfaces of cognitive assistance are grouped into three levels: soft aid, soft intervention, and hard intervention. This paper took driving assistance as an exemplary application to validate the approach of cognitive assistances coordination. In the experiment, an intelligent machine driver monitored drivers’ real-time performance by measuring the time headway to front obstacles and the lateral deviation to lane center. Simultaneously, it monitored drivers’ cognitive state by measuring the eye movement with an eye tracker. With five sessions of driving, coordinated cognitive assistance was compared with no aid, soft aid, soft intervention, and hard intervention, respectively. The experimental results confirmed that coordinated cognitive assistance is the most effective approach to assist both primary and secondary tasks. It also proves to be a more enjoyable and less obtrusive assistance system when compared to other individual types of assistance. In addition, coordinated cognitive assistance can be extended to other real-time control relevant tasks.

scholarly journals Cyberattacks and Countermeasures for In-Vehicle Networks

2021 ◽  
Vol 54 (1) ◽  
pp. 1-37 ◽  
Author(s):  
Emad Aliwa ◽  
Omer Rana ◽  
Charith Perera ◽  
Peter Burnap
Keyword(s):  
Real Time ◽  
Can Bus ◽  
Mitigation Strategies ◽  
Area Network ◽  
Frame Size ◽  
Control Units ◽  
Time Sensitivity ◽  
Small Frame ◽  
Entry Points ◽  
Attack Surface

As connectivity between and within vehicles increases, so does concern about safety and security. Various automotive serial protocols are used inside vehicles such as Controller Area Network (CAN), Local Interconnect Network (LIN), and FlexRay. CAN Bus is the most used in-vehicle network protocol to support exchange of vehicle parameters between Electronic Control Units (ECUs). This protocol lacks security mechanisms by design and is therefore vulnerable to various attacks. Furthermore, connectivity of vehicles has made the CAN Bus vulnerable not only from within the vehicle but also from outside. With the rise of connected cars, more entry points and interfaces have been introduced on board vehicles, thereby also leading to a wider potential attack surface. Existing security mechanisms focus on the use of encryption, authentication, and vehicle Intrusion Detection Systems (IDS), which operate under various constraints such as low bandwidth, small frame size (e.g., in the CAN protocol), limited availability of computational resources, and real-time sensitivity. We survey and classify current cryptographic and IDS approaches and compare these approaches based on criteria such as real-time constraints, types of hardware used, changes in CAN Bus behaviour, types of attack mitigation, and software/ hardware used to validate these approaches. We conclude with mitigation strategies limitations and research challenges for the future.

scholarly journals Operating System Realization for Real-Time Visualization of CAN-Bus Data Streams using Xilinx Zync SoC

2020 ◽  
Vol 4 (2) ◽  
pp. 44
Author(s):  
Mohammad J. M. Zedan
Keyword(s):  
Operating System ◽  
Real Time ◽  
Can Bus ◽  
Design Procedure ◽  
Area Network ◽  
Development Platform ◽  
System Realization ◽  
Real Time Visualization ◽  
Communications Protocol ◽  
External Signals

The revolution in the automotive industry over time led to more and more electronics to be included in the vehicle and this increased the number and space allocated for cables. Therefore, the in-vehicle cabling network has been replaced with a two-wire bus serial communications protocol called Controller Area Network (CAN). The proposed paper describes the implementation of the CAN controller as a listener to monitor the state of the CAN bus in a real-time approach. The CAN listener obtains the data from the CAN bus by using an external signals converter. The work is realized using development platform called ZedBoard. The controller performs a sequence of processes on the received CAN frames including decoding, buffering and filtering. The processed data is stored in an implemented FIFO to keep the data from loss. After that, the data is sent serially to the processor system over the implemented SPI that connects the controller with the processor of the Zynq-7000 device. A single-threaded, simple operating system is run over the processor to provide a set of libraries and drivers that are utilized to access specific processor functions. It enables the execution of the C code that was written to configure the operation of the onboard display unit. The design procedure and simulation process for the implemented CAN listener is achieved using the Xilinx ISE WebPACK environment, while the final complete design is properly tested and verified by connecting the module to a CAN network consisting of six CAN nodes.

Scheduling the CAN Bus with Rate Monotonic Techniques

2011 ◽  
Vol 383-390 ◽  
pp. 4318-4322
Author(s):  
Zai Ping Chen ◽  
Yan Lei Guo
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Can Bus ◽  
Scheduling Algorithm ◽  
Data Networks ◽  
Area Network ◽  
Factory Automation ◽  
Rate Monotonic ◽  
Network Utilization ◽  
Periodic Data

Controller Area Network (CAN) is widely used in real-time automobile control and is gaining wider acceptance as a standard for factory automation. This paper discusses the applicability of Rate Monotonic (RM) techniques to the scheduling of CAN messages. Rate Monotonic can guarantee higher network utilization, but it is difficult to implement in periodic data networks or local buses. The paper mainly analyzes the RM scheduling algorithm and then establishes the simulation model about the algorithm based on CAN bus, and analyzes the effect in this situation of experiment.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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