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India is one of the largest markets for the automobile sector and considering the trends of road fatalities and injuries related to road accidents, it is pertinent to continuously review the safety regulations and introduce standards which promise enhanced safety. With this objective, various Advanced Driver Assistance Systems (ADAS) regulations are proposed to be introduced in the Indian market. ADAS such as, Anti-lock Braking Systems, Advanced Emergency Braking systems, Lane Departure Warning Systems, Auto Lane Correction Systems, Driver Drowsiness Monitoring Systems, etc., assist the driver during driving. They tend to reduce road accidents and related fatalities by their advanced and artificial intelligent fed programs. This paper will share an insight on the past, recent trends and the upcoming developments in the regulation domain with respect to safety.

Autonomous Emergency Braking (AEB) systems play a critical role in ensuring vehicle safety by detecting potential rear-end collisions and automatically applying brakes to mitigate or prevent accidents. This paper focuses on establishing a framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems (ADAS) by testing & verifying the functionality of a RADAR-based AEB ECU. A comprehensive V&V approach was adopted, incorporating both virtual and physical testing. For virtual testing, closed-loop Hardware-in-Loop (HIL) simulation technique was employed. The AEB ECU was interfaced with the real-time hardware via CAN. Data for the relevant target such as the target position, velocity etc. was calculated using an ideal RADAR sensor model running on the real-time hardware. The methodology involved conducting a series of test scenarios, including various driving speeds, obstacle types, and braking distances.

The paper talks about Quantification of Alertness for vision based Driver Drowsiness and Alertness Warning System (DDAWS). The quantification of alertness, as per Karolinska Sleepiness Scale (KSS), reads the basic input of facial features & behaviour recognition of driver in a standard manner. Although quantification of alertness is inconclusive with respect to the true value, the paper emphasised on systematic validation process of the system covering various scenarios in order to evaluate the system’s functionality very close to the reality. The methodology depends on definition of threshold values of blink and head pose. The facial features are defined by number of blinks with classification of heavy blink and light blink and head pose in (x, y, z) directions. The Human Machine Interface (HMI) warnings are selected in the form of visual and acoustic signals. Frequency, Amplitude and Illumination of HMI alerts are specified.

The Indian automotive industry is striving towards more safe and durable vehicles. A need was felt to study the effect of changes in axle static loads on fatigue life of the axle components. Also, there was a need to develop generic test method, as there are no test standards or generic methods available in public domain for fatigue testing of commercial vehicle axles. The study was carried out to check direct effect of change in axle loads on various connections on axle, effect of suspension configuration and force distribution, Vehicle dynamics, etc. In this paper, an India specific generic load spectra was evaluated for accelerated laboratory validation. Paper discusses the methodology as; study of heavy commercial vehicle systems, road load data collection on identified test vehicles w.r.t. test matrix finalized, India specific test loads and load spectra development, normalization of axle load spectra w.r.t to static axle weights and arriving at test guidelines.

The term Industry 4.0 is well known in contemporary automotive landscape. It encompasses a smart integrated framework of IIoT (Internet of Things) and industrial automation with machine learning, artificial intelligence and big data analytics to arrive at optimal solutions to running the processes in a streamlined, efficient and effective manner. Industry 4.0 has assumed critical significance in the contemporary era of people working from remote locations to operate processes in order to build products, thereby ensuring business continuity. Consequently, it follows that if industry 4.0 is applied to automotive homologation activity, it will lead to a standardized evaluation, consistent fidelity of testing, accurate judgement of the product under test with regards to its certification, and most importantly, timed delivery to release in the market. The author hereby elucidates a unified Industry 4.0 Framework for Automotive homologation in India which is the need of the hour.

Wheel rims and wheel hub bearings are critical components of Heavy Commercial Vehicle (HCV) suspension systems and are subjected to extensive fatigue loading throughout their operational life. Actual loading conditions on wheels are a combination of radial loads (vertical loads) and cornering loads (lateral loads) acting simultaneously and are directly influenced by payload and road conditions. Currently for Indian usage, there are test guidelines [1] only for separate uniaxial Radial Fatigue Test (RFT) and Cornering Fatigue Test (CFT) for wheel rims which might not represent realistic combined loading conditions, and no generic guidelines are available for testing of wheel hub bearings.

Automotive electronics is increasingly playing a vital role in all vehicle subsystems. Since an electronic control system needs to be interfaced with the outside world, an electronic smart switch forms a key output interface with various loads such as solenoids, lamps, motors, relays, fans etc. Although integrated circuit based smart-switch semiconductor solutions are provided by all global semiconductor vendors, they prove more often than not to be overdesigned for majority of situations relevant to low end vehicles. They are also generously loaded with standard high-end features like thermal and overload protection which may not always be required. In addition, external transient protection and on-chip diagnostic features lend further complexity to the entire solution.

Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology. ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision. There are number of open source annotated datasets available viz. COCO, KITTI etc.

Tire failure is identified as a major cause of accidents on highways around the world in the recent past. A tire burst leads to loss of control of the vehicle which ends up in a catastrophe. There are various factors which are accounted for a tire burst. Heat buildup, aging of tire and cracks on tires are the major ones which are identified. A superior ability of the tire to dissipate the heat generated during operation is a major factor which prevents a tire failure. Other factors such as ambient temperature, inflation pressure etc. contributes to heat buildup which may ultimately result in tire failure. A combination of these factors might manifest as a tire failure at high speeds, the latter being an immediate cause of heat buildup. A dormant crack in the tire might develop if the temperature and pressure conditions are favorable, thus giving away at the weakest point. With regard to the temperature conditions, road conditions, inflation pressure checks etc.

Designers and analysts need to compare and conduct synthesis for selection of materials based on their properties involving simulation, optimization and correlation with test data. An example is that of acoustic material properties such as random and normal incidence sound absorption coefficient and sound transmission loss. The international test standards necessitate having standard operating procedures for characterization of these materials. This procedure is quite involved and addresses steps including test data acquisition, post processing, calculations, classification, report generation and most importantly, storage of such innumerable material properties in a structured manner to facilitate ease of retrieval and updating of properties. It is also highly desirable to have a synergy of the databank directly with simulation tools. Further, all of these steps need to be accurate, non-speculative and quick.