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In general for Vehicle-to-Vehicle (V2V) communication, message authentication is performed on every received wireless message by conducting verification for a valid signature, and only messages that have been successfully verified are processed further. In V2V safety communication, there are a large number of vehicles and each vehicle transmits safety messages frequently; therefore the number of received messages per second would be large. Thus authentication of each and every received message, for example based on the IEEE 1609.2 standard, is computationally very expensive and can only be carried out with expensive dedicated cryptographic hardware. An interesting observation is that most of these routine safety messages do not result in driver warnings or control actions since we expect that the safety system would be designed to provide warnings or control actions only when the threat of collision is high.

Software programs in non-application areas such as Board Support Packages, Hardware Abstraction Layers, signal processing and data acquisition are more or less very standard and common across many applications. These form a major part of the “platform” software, which changes very little. However, it is seen that many a time, efforts are spent resolving issues in the hardware dependent layers rather than concentrating on the application at hand, despite the fact that the software controlling the hardware has been developed many times. There are many reasons why this section of the software is rewritten many times over: different coding standards, different software architecture and layering concepts, the dreadful cut-and-paste methods, and so on. Introduction of a tool-based code configurator and generator eliminates access to the code and focuses on configuring a pre-written set of SW procedures. Advantages: Standardization, reuse and high levels of productivity.

As increasing populations set demands for the abundant natural resources of the colder regions, the complex industrial machines of American Industry are expected to build the new railroads, roads, cities, mines, power sources, airports and factories, and operate these activities year round. This paper reviews the basic problems of “winterization” and the design practices, materials and components used with varying effectiveness in “winterizing” construction and associated equipment for use in moderate and extreme cold geographical areas. Several new charts are included which offer design temperature data for the northern United States, Canada, Alaska, Greenland, as well as average January isotherms around the earth. The authors are members of CIMTC Subcommittee XV, “Environment”.

The SHIFT-MATE is a dashboard mounted computer based device that cues a truck driver to shift more efficiently. Through electronic circuitry, key vehicle parameters are monitored, computed, then via graphic display, instructs the driver when to shift for improved fuel economy. The theory of operation is described in the text.

There are many different vibration sources in a car. Engine, gears, road roughness, impacts against the wheels cause vibration and sound that can decrease the parts and the car durability as well as affect drivability, safety and passengers and community comfort. In 4×4 cars, some extra vibration sources are the parts responsible for transmitting the torque and power to the rear wheels. Each of them has their own vibration modes, excited mostly by its imbalance or by the second order engine vibration. The engine vibration is a very well known phenomena and the rear driveshaft is designed not to have any vibration mode in the range of frequencies that the engine works or its second order. The imbalance of a driveshaft is also a design requirement. That means, the acceptable imbalance of the driveshaft is limited to a maximum value.

Posture and gait controls underlie the fundamental physical and cognitive human factors necessary for astronauts’ safety and performance in Space. This central subsystem is adversely affected when exposed to an extreme or hostile environment. A specific stimulation, using dermal optical sensitivity, can be provided to the central nervous system to counteract peripheral stimulations due to microgravity as well as other negative stressors. We believe using dermal optical sensitivity-based stimulation can be key in the performance enhancement necessary to ensure human based space mission viability and success.

A sense of “personal integrity” blocks pilot use of new information about how he thinks. Research on human performance under stress done over the past fifty years indicates increased rigidity and regression to earlier learned behavior in high stress, and in low Stress a shift in attention to any domestic situation or on the job controversy which is of higher stress than that of the job at hand, all without the pilot's knowledge. Informal surveys of commercial pilot training and commercial pilot attitudes towards these studies indicate that the study findings directly confront learned cultural responses. Pilot and trainer reactions prevent the information from being adequately investigated or formally taught. The findings are not written into training manuals and pilots who are informally given the information do not have adequate access to the knowledge when it is needed.

A large number of testing procedures have been developed to ensure vehicle safety in common and extreme driving situations. However, these conventional testing procedures are insufficient for testing autonomous vehicles. They have to handle unexpected scenarios with the same or less risk a human driver would take. Currently, safety related systems are not adequately tested, e.g. in collision avoidance scenarios with pedestrians. Examples are the change of pedestrian behaviour caused by interaction, environmental influences and personal aspects, which cannot be tested in real environments. It is proposed to use augmented reality techniques. This method can be seen as a new (Augmented) Pedestrian in the Loop testing procedure.

A multi vector design tool to accurately predict instrument panel obscuration was developed to insure that critical legal displays in vehicles are not obscured. The concept provides for a computer generated light source shaped to replicate the human eyes. The light source is then projected onto a 3D math based arrangement and the resultant shadows are visible on the instrument panel surface and its displays. Design studios require criteria for the placement of the instrument cluster gages and displays, various controls, switches, and steering column stalks before an interior theme can be completed. Therefore, instrument panel obscuration and visibility must be determined early in the design process. The obscured areas are a function of the instrument panel surface, steering wheel rim, hub, spokes, and the location of the driver's eyes. This light source method allows engineers and designers the ability to quickly determine obscured areas.

Vehicle dynamics is a discipline of mechanical engineering that benefited of significant improvements thanks to the progress of computational engineering. Vehicle dynamics engineers are using CAE for the development of a vehicle with MBS and FEA. The concurrent use of these two technologies is a standard in the automotive industry. However the current simulation process is not fully efficient because local geometrical and material nonlinearities are not accurately modeled in classical MBS software. This paper introduces a methodology for vehicle dynamics simulation integrating MBS capabilities in one single nonlinear FEA environment enabling an accurate modeling of nonlinearity in vehicles.

The results of several anti-knock studies are discussed in this paper. Road anti-knock performance for 1000 fuel blends covering the years 1940 to 1957 have been investigated. The laboratory Research octane numbers of these fuels covered the range from 80 to 105. The fuels were evaluated in 46 cars representing a cross-section of the automotive products for these years. The objective of these investigations was to determine the practical application of the laboratory to road octane rating relationships, and the effect of vehicles, and operating conditions on these relationships. The results show that there is a valid correlation between laboratory and road octane ratings. The relative importance of Research and Motor octane ratings on road performance is influenced by make of car, engine speed, throttle position, and distributor advance characteristics. It also indicated that aromatics improve, whereas olefins reduce high speed Modified Borderline ratings.

In recent years there has been increasing interest in quantifying the emissions from aircraft in order to generate inventories of emissions for climate models, technology and scenario studies, and inventories of emissions for airline fleets typically presented in environmental reports. The preferred method for calculating aircraft engine emissions of NOx, HC, and CO is the proprietary “P3T3” method. This method relies on proprietary airplane and engine performance models along with proprietary engine emissions characterizations. In response and in order to provide a transparent method for calculating aircraft engine emissions non proprietary fuel flow based methods 1,2,3 have been developed. This paper presents derivation, updates, and clarifications of the fuel flow method methodology known as “Fuel Flow Method 2”.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

Driver state monitoring is a crucial technology for enhancing road safety and preventing human error-caused accidents in the era of autonomous vehicles. This paper presents CogniSafe, a comprehensive driver monitoring system that uses deep learning and computer vision methods to detect various types of driver distractions and fatigue. CogniSafe consists of four modules: Driver anomaly detection and classification: A novel two-phase network that proposes and recognizes driver anomalies, such as texting, drinking, and adjusting radios, using multimodal and multiview input. Gaze estimation: A video-based neural network that jointly learns head pose and gaze dynamics, achieving robust and efficient gaze estimation across different head poses. Eye state analysis: A multi-tasking CNN that encodes features from both eye and mouth regions, predicting the percentage of eye closure (PERCLOS) and the frequency of mouth opening (FOM).

This paper is an extension of our previous work on the CHASE (Classification by Holistic Analysis of Scene Environment) algorithm, that automatically classifies the driving complexity of a road scene image during day-time conditions and assigns it an ‘Ease of Driving’ (EoD) score. At night, apart from traffic variations and road type conditions, illumination changes are a major predominant factor that affect the road visibility and the driving easiness. In order to resolve the problem of analyzing the driving complexity of roads at night, a brightness detection module is incorporated in our end-to-end nighttime EoD system, which computes the ‘brightness factor’ (bright or dark) for that given night-time road scene. The brightness factor along with a multi-level machine learning classifier is then used to classify the EoD score for a night-time road scene. Our end-to-end ‘Night-time EoD system’ is a real-time onboard system implemented and tested on road scene data collected in Japan.

Affordable, efficient and durable catalytic converters for the Commercial Vehicle and Non-Road industry in all countries are required to reduce vehicle emissions under real world driving conditions and fulfill future legal requirements. Specially for India traffic conditions and payload to engine size conditions new cost-effective solutions are needed to participate in a cleaner and healthier environment. Metallic substrates with structured foils like the Transversal StructureTM (TS) or the Longitudinal StructureTM (LS) have been proved to be capable of improving conversion behavior, even with smaller catalyst size. Now Vitesco Technologies is developed a new Substrate for Heavy duty applications that specifically maintains the geometric surface area at a very high level and improves further the mass transport of the pollutants, which potentially leads together to very high pollutant conversion rates.

A new truck type street sweeper has been developed which incorporates some of the sweeping advantages of a three wheeled sweeper (tricycle steer) and the transport advantages of a legal highway truck. It offers major productivity improvements through better operator environment and decrease of nonsweeping time in the operational cycle. It is possible for a small “short line” special purpose vehicle manufacturer to develop, test, and produce such a vehicle and meet Federal regulatory requirements with limited “In house” design and testing facilities. Here this was accomplished through judicious augmentation by outside specialized design and testing organizations.