Chip Design for In-Vehicle System Transmitter

This paper presents embedded system design of the In-Vehicle System (IVS) for the European Union (EU) emergency call (eCall) system. The IVS transmitter modules are designed, developed and implemented on a field programmable gate array (FPGA) device. The modules are simulated, synthesized, and optimized to be loaded on a reconfigurable device as a system-on-chip (SoC) for the IVS electronic device. All the modules of the transmitter are designed as a single embedded module. The bench-top test is completed for testing and verification of the developed modules. The hardware architecture and interfaces are discussed. The IVS signal processing time is analyzed for multiple frequencies. A range of appropriate frequency and two hardware interfaces are proposed. A state-of-the-art FPGA design is employed as a first implementation approach for the IVS prototyping platform. This work is used as an initial step to implement all the modules of the IVS on a single SoC chip.

A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

Recent Trends of In-Vehicle Time Sensitive Networking Technologies, Applications and Challenges

With the vigorous development of automobile industry,in-vehicle network is also constantly upgraded to meet data transmission requirements of emerging applications.The main transmission requirements are low latency and certainty especially for autonomous driving.Time sensitive networking(TSN)based on Ethernet gives a possible solution to these requirements.Previous surveys usually investigated TSN from a general perspective,which referred to TSN of various application fields.In this paper,we focus on the application of TSN to the in-vehicle networks.For in-vehicle networks,we discuss all related TSN standards specified by IEEE 802.1 work group up to now.We further overview and analyze recent literature on various aspects of TSN for automotive applications,including synchronization,resource reservation,scheduling,certainty,software and hardware.Application scenarios of TSN for in-vehicle networks are analyzed one by one.Since TSN of in-vehicle network is still at a very initial stage,this paper also gives insights on open issues,future research directions and possible solutions.

Measurement of in-vehicle volatile organic compounds under static conditions

The types and quantities of volatile organic compounds （VOCs） inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spectrometer （TD-GC/MS）. Air sampling and analysis was conducted under the requirement of USEPA Method TO-17. A room-size, environment test chamber was utilized to provide stable and accurate control of the required environmental conditions （temperature, humidity, horizontal and vertical airflow velocity, and background VOCs concentration）. Static vehicle testing demonstrated that although the amount of total volatile organic compounds （TVOC） detected within each vehicle was relatively distinct （4940 μg/m^3 in the new vehicle A, 1240 μg/m^3 in used vehicle B, and 132 μg/m^3 in used vehicle C）, toluene, xylene, some aromatic compounds, and various C7-C12 alkanes were among the predominant VOC species in all three vehicles tested. In addition, tetramethyl succinonitrile, possibly derived from foam cushions was detected in vehicle B. The types and quantities of VOCs varied considerably according to various kinds of factors, such as, vehicle age, vehicle model, temperature, air exchange rate, and environment airflow velocity. For example, if the airflow velocity increases from 0.1 m/s to 0.7 m/s, the vehicle＇s air exchange rate increases from 0.15 h^-1 to 0.67 h^-1, and in-vehicle TVOC concentration decreases from 1780 to 1201 μg/m^3.

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

An Energy Efficient Routing Protocol for In-Vehicle Wireless Sensor Networks

In this paper, an advanced distributed energy-efficient clustering (ADEEC) protocol was proposed with the aim of balancing energy consumption across the nodes to achieve longer network lifetime for In-Vehicle Wireless Sensor Networks (IVWSNs). The algorithm changes the cluster head selection probability based on residual energy and location distribution of nodes. Then node associate with the cluster head with least communication cost and high residual energy. Simulation results show that ADEEC achieves longer stability period, network lifetime,and throughput than the other classical clustering algorithms.

In-Vehicle Network Injection Attacks Detection Based on Feature Selection and Classification

Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle networks.The transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols regulations.Typically,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal data.However,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network attacks.In this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection method.In consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is tested.In addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal data.This paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data packets.The experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.

Photocatalytic decomposition of mobile-source related pollutants using a continuous-flow reactor

This study evaluated the application of a continuous-flow photocatalytic reactor for the control of two mobile-derived pollutants, methyl-tertiary butyl ether （MTBE） and naphthalene, present at in-vehicle levels. Variables tested for this study included the hydraulic diameter （HD）, stream flow rate （SFR）, relative humidity （RH）, and feeding type （FT）. The fixed parameters included contaminant concentration, ultraviolet light source, and the weight of TiO2. In all experimental conditions the adsorption process reached equilibrium within 30 to 180 min for the target compounds, and the outlet concentrations of the photocatalytic oxidation （PCO） reactor while operating reached a steady state within 60 to 180 min. The degradation of the target compounds was dependent on RH, HD, FT, or SFR. The PCO system exhibited high degradation （up to nearly 100% for certain conditions） and mineralization efficiencies of target compounds, suggesting that this system can effiectively be employed to improve indoor air quality. Moreover, it was confirmed that trichloroethylene at urban-ambient level also could enhance the degradation efficiency of naphthalene when applying the PCO technology inside vehicles.

Modified Vanstone’s Construction of lightweight MAC for Vehicular On-Board IT Systems

We propose a lightweight construction, a modification of Vanstone’s MAC construction, for the message authentication of the communication between Electronic Control Units (ECUs) in distributed car control systems. The proposed approach can solve the task of error control and authentication in unified algorithmic technology, called MAC (Message Authentication Code) with ECC (Error Correction Code). We follow a provable approach in the design of the cryptographic primitive, where we quantify the security measures in the parameters of the system. Provable security approaches are missing in the field of secure in-vehicle communication.

Evaluating the moderating effect of in-vehicle warning information on mental workload and collision avoidance performance

Purpose–The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers.Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload.However,whether moderation and mediation effects exist among warning information,driver’s cognition,behavior and risky avoidance performance is unclear.Design/methodology/approach–This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior.A driving simulator experiment was conducted with waring and command information.Findings–Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario.The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk.At the same time,command information can weaken this negative effect.Moreover,improved collision avoidance behavior can be achieved through increasing drivers’mental workload.Practical implications–The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior,thus contributing to improved in-vehicle information system design.Originality/value–The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’collision avoidance ability.Through further calibration with larger sample size,the proposed structural model can be used to predict the effect of invehicle warnings in different risky driving scenarios.

Review of In‑Vehicle Optical Fiber Communication Technology

With the continuous development of automotive intelligent networking and autonomous driving technologies,the number of in-vehicle electronic systems and applications is increasing rapidly.This change increases the amount of data to be transmitted in the vehicle and puts forward further requirements of higher speed and safety for in-vehicle communication.Traditional vehicle bus technologies are no longer sufficient to meet today’s high-speed transmission requirements,in which copper cables are used extensively,resulting in serious electromagnetic interference(EMI).Vehicle optical fiber communication technology,besides greatly improving the data transmission rate,has the advantages of anti-EMI,reducing cable space and vehicle mass.This paper first presents the motivation of applying vehicle optical fiber communication technology and reviews the development history of vehicle optical fiber communication technology.Then,the paper researches the devel-opment trend of automotive electrical and electronic architecture(EEA),from distributed EEA to domain centralized EEA and zone-oriented EEA.Based on the discussion of the development trend of automotive EEA,an EEA based on vehicle optical fiber communication technology is proposed.Finally,the key points and future directions of vehicle optical fiber communication technology research are highlighted,including vehicle multi-mode optical fiber technology,vehicle optical fiber network protocol,and topology.

A four-dimensional interaction-based appraisal approach towards the performance enhancement of a vehicular waste heat recovery system

The non-linear multifactorial impacts on fuel-saving potential constrain the practical performance of the vehicular waste heat recovery system(WHRS). This study proposed a four-dimensional interaction-based appraisal approach to interpreting these impacts for enhancing WHRS's in-vehicle performance. The interaction incorporates a heat exchanger, configuration, engine,and vehicle. The proposed approach comprises two successive steps, emphasizing evaluation under the rated(Step 1) and off-design(Step 2) heat source conditions. A case study of waste heat recovery from a passenger vehicle was conducted to evaluate the in-vehicle performance of a novel co-split system and two single-split ones(with/without a regenerator) through this approach. The novel system theoretically modifies vehicular performance but remains ambiguous concerning real-world behaviour, which is assessed and verified by the proposed approach. Two key factors determining vehicular performance were identified by Step 1, namely, net power output and engine backpressure. As the co-split system modified both factors, its fuel-saving potential could be increased by up to 20.3% compared with single-split systems. Also, the limiting factor for off-design performance was pinpointed by Step 2, namely, the mismatch between the heat source and working fluid, which led to the solution, i.e., the synergistic split regulation of the working fluid and heat source. An up to 8.8% improvement in net power output was achieved by the co-split system at off-design heat sources compared with fixed split ratios. Consequently, the approach enables holistic performance improvement of the vehicular WHRS under design/off-design heat source conditions.

Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles

Design of an Ethernet network compatible data acquisition system for the measurement of yaw rate and longitudinal velocity in automobiles is presented.The data acquisition system includes a base node and a remote node.The remote node consists of a micro electro mechanical system(MEMS)accelerometer,an MEMS gyroscope,an advanced RISC machines(ARM)CORTEX M3 microcontroller and an Ethernet PHY device.The remote node measures the yaw rate and the longitudinal velocity of an automobile and sends the measured values to the base node using Ethernet communication.The base node consists of an ARM CORTEX M3 microcontroller and an Ethernet PHY device.The base node receives the measured values and saves in a microSD card for further analysis.The characteris tics of the net work and the measurement system are stu died and repor ted.

Safety effects of work zone advisory systems under the intelligent connected vehicle environment:a microsimulation approach

Purpose–This study aims to investigate the safety effects of work zone advisory systems.The traditional system includes a dynamic message sign(DMS),whereas the advanced system includes an in-vehicle work zone warning application under the connected vehicle(CV)environment.Design/methodology/approach–A comparative analysis was conducted based on the microsimulation experiments.Findings–The results indicate that the CV-based warning system outperforms the DMS.From this study,the optimal distances of placing a DMS varies according to different traffic conditions.Nevertheless,negative influence of excessive distance DMS placed from the work zone would be more obvious when there is heavier traffic volume.Thus,it is recommended that the optimal distance DMS placed from the work zone should be shortened if there is a traffic congestion.It was also revealed that higher market penetration rate of CVs will lead to safer network under good traffic conditions.Research limitations/implications–Because this study used only microsimulation,the results do not reflect the real-world drivers’reactions to DMS and CV warning messages.A series of driving simulator experiments need to be conducted to capture the real driving behaviors so as to investigate the unresolved-related issues.Human machine interface needs be used to simulate the process of in-vehicle warning information delivery.The validation of the simulation model was not conducted because of the data limitation.Practical implications–It suggests for the optimal DMS placement for improving the overall efficiency and safety under the CV environment.Originality/value–A traffic network evaluation method considering both efficiency and safety is proposed by applying traffic simulation.