Search Results

Active control mainly comprises of three parts; sensor-detects the input disturbance, actuator -provide counter measures and control logic -processing of input disturbances and converting it into logical output. Lot of methods for active vibration control are available but this paper deals with active control of steering wheel vibrations of an LCV. A steering wheel is, one such component that directly transfers vibration to the driver. Active technique described here is implemented using accelerometer sensor, IMA (Inertial Mass Actuator) and feed forward Fx-LMS (Filtered reference Least Mean Square) control algorithm. IMA is a single-degree-of-freedom oscillator. To enable a control, IMA needs to be coupled to the structure at a single point, acting as an add-on to the passive system. Fx-LMS is a type of adaptive algorithm which is computationally simple and it also includes compensation for secondary path effects by using an estimate of the secondary path.

Occupant motion in a vehicle rollover accident is a function of many factors. Some important ones are vehicle kinematics, position of the occupant in the vehicle, occupant size, ground topology and restraint usage. The far side belted occupants are more vulnerable than the near side occupants in a rollover accident as they have more energy as a result of their trailing and higher side of the vehicle. This outcome is attributable to the inadequate safety performance of the conventional single loop; B-pillar mounted D-ring restraints. Roof crush tends to displace the vehicle's B-pillar, resulting in D-Ring displacement which causes slack in the lap portion of the restraint. This slack enables centrifugal loads to move the far side occupant further away from the vehicle's instantaneous point of rotation. In this scenario, the presence of any ejection portal can result in an occupant becoming partially or fully ejected.

In year 2015, 17 people were killed every hour by road accidents in India [1]. The occurrence of road accidents is observed to be higher during night, when visibility is at its lowest. The two factors which affect visibility are insufficient illumination and glare caused by the oncoming traffic. The Adaptive Front Lighting System [AFS] is an active safety feature which addresses these problems by employing specific lighting modes for Town, Country, Expressway conditions and automatic switching between Driving Beam and Passing Beam whenever required. Matrix of LEDs or a Projector with an actuator or a combination of both is employed in achieving different Lighting modes. The projector based AFS module is preferred for implementing the AFS control logic for passing beam owing to its economic cost.

Frontal crash is the most common type of accidents in passenger vehicles which results in severe injuries or fatalities. During frontal crash, some frontal vehicle body has plastic deformation and absorbs impact energy. Hence vehicle crashworthiness is important consideration for safety aspect. The crash box is one of the most important parts in vehicle frontal structure assembly which absorb crash energy during impact. In case of frontal crash accident, crash box is expected to be collapsed by absorbing crash energy prior to the other parts so that the damage to the main cabin frame and occupant injury can be minimized. The main objective of this work is to design and optimize the crash box of passenger vehicle to enhance energy absorption. The modeling of the crash box is done in CATIA V5 and simulations are carried out by using ANSYS. The results show significant improvement in the energy absorption with new design of the crash box and it is validated experimentally on UTM.

One of the primary reasons for road accidents is driving while distracted or drowsy. Often, long and monotonous road journeys lead to distracted or drowsy driving. Therefore, there is a need for a system which alerts a distracted or drowsy driver. Moreover, as the levels of autonomy move beyond SAE Level 2, the system assumes a larger share of the dynamic driving task. Under challenging circumstances, the system might ask the driver to take back vehicle control. To guarantee safety, it’s crucial to monitor the driver’s condition in order to assess their readiness to regain control of the vehicle. An advanced safety feature known as a driver monitoring system (DMS), sometimes referred to as a driver state sensing (DSS) system, is designed to monitor a driver’s attentiveness and alertness, providing warnings or alerts to refocus their attention on driving when drowsiness or distraction is detected.

Design of vehicle for targeted customer usage is one of the key steps during vehicle development process. Due to globalization, most of vehicles, aggregates, components are being designed for global market considering worldwide load spectrum. Generally for doing this the vehicle response is being measured for different markets but this process is very time consuming. Also for getting these vehicle dependent parameters, exercises need to be repeated on each type/class of vehicle. So there is a need to have a robust procedure, tools which will helps OEM’s to predict the loads, vehicle response for different market segments at an early stage of vehicle development program using the inputs which are vehicle independent. The solution for this could be to use vehicle independent input such as digitized road profiles (2D or 3D) of target customer markets in combination with proper MBD simulation tools.

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.

This paper presents a methodology for predicting thermal comfort inside Midibus cabin with an objective to modify the Heating, Ventilation and Air Conditioning (HVAC) duct design and parametric optimization in order to have improved thermal comfort of occupant. For this purpose the bus cavity is extracted from baseline CAD model including fully seated manikins with various seating positions. Solar Load has been considered in the computational model and passenger heat load is considered as per BSR/ASHRAE 55-1992R standard. CFD simulation predicted the air temperature and velocity distribution inside passenger cabin of the baseline model. The experimental measurements have been carried out as per the guidelines set in APTA-BT-RP-003-07 standard. The results obtained from CFD and Experimental test were analysed as per EVS EN ISO7730 standard and calculated occupant comfort in terms of thermal comfort parameters like Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD).

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 Construction & Mining field is continuously upgrading, reshaping under the stimulus of technical enhancement. India is considered one of fastest growing country in the word. Requirement for Construction Equipment Vehicles in India is continuously growing due increased rate infrastructure development. To promote development of the Construction Equipment Vehicles (CEV’s) manufacturing sector it was also necessary to build a new governance architecture. Every vehicle plying on road has to comply with Central Motor Vehicle Regulatory requirements as per CMVR act 1989. Earlier 2021 CEV’s were required to go through performance trials like brake, steering effort, turning circle measurement, speedometer calibration as dynamic tests as per regulations.

Sound Quality (SQ) of brake and clutch pedal assembly plays an important role in contributing to vehicle interior noise and perception of sound. Quiet operation of brake and clutch units also reflects the vehicle built and material quality. Noise emitted from these sub-assemblies has to meet certain acceptance criteria as per different OEM requirements. Not much work has been carried on this over the years to characterize and quantify the same. An attempt has been made in this paper to study the sound quality of brake and clutch pedal assemblies at component level and validate the same by identifying the parameters affecting SQ. Effect on noise at different environmental conditions was studied with typical operating cycles in a hemi-anechoic chamber. The effect of sensor switches integrated within the clutch and brake pedal on sound quality is analyzed. It is found that the operating characteristics of switches drives the noise and SQ.