Road conditions basically through exhibiting acceleration, breaking and

Road accidents statistics clearly pose a
challenge on three main aspects: Human Error, Vehicle failures and Road
conditions. Human errors being most influential factor for cause of accidents,
serious attention needs to be paid on detection, analysis and monitoring of
driver behaviour. Understanding driver’s behaviour is the key factor which
contributes towards the road safety. Moreover if the driver’s behaviour is
recorded and analyzed, it could have influential positive impact on the system.
Hence the methods for detection and monitoring of behaviour exhibited by driver
are of significant interest. By doing so, compliance to driving regulation
could be achieved and this may help achieve the goal of road and driver safety.


Driver has to dynamically interact to the
road surface and traffic conditions to control the vehicle. It is very natural
that different driver’s will respond differently to the similar driving
conditions basically through exhibiting acceleration, breaking and steering.
Vehicle Driver’s should be able to identify the vehicle dynamics in terms of
position, velocity, acceleration, orientation and direction of the vehicle and also
keep a watch whether change in the relationship between these factors is
leading to an risky condition either for occupants of the vehicle or other road
users.  The most stable ride is the
safest ride. In most of the cases it becomes unrealistic to set a same hard
limit for all vehicles or for all drivers. As every driver shows a behaviour
which is unique, it becomes necessary to change the limits based on the type of
vehicle and the limit can be specific to category of them. By collecting and
analyzing the vehicle manoeuvring data, driving behaviours could be classified
as aggressive or normal behaviour. To ensure overall stability in vehicle
motion, an indicative measure is of high significance.

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For fleet management applications identifying
abnormal driving manoeuvres is definitely an important research focus due to
its significant impact on fuel consumption and road safety. The in-vehicle
sensing and Internet-of-Vehicles (IoV) technologies put together are capable of
collecting abundant IMU data viz. longitudinal accelerations and lateral
acceleration, driving data such as speed, steer angle and engine parameters,
from a large number of vehicles. Such data are categorized as large volume, multi
domain, multi-frequency, and multi-source, which mainly reflect the vehicle
status and thereby are extensively used to assess driving behaviours. (Mingming
et al., 2017)


Some insurance companies provide extended
warranty on range of parts in vehicle and it becomes necessary for them to know
whether these parts were used properly as advised. The advantage of detecting
the abnormal driving behaviours for insurance companies is providing a new
‘pay-as-you-drive’ service to clients by collecting dynamic data and judging
their driving manoeuvres. Collected data is then analyzed and thus fleet-operating
companies can regulate their drivers to act more wisely while riding car,
lowering the accidental risk and fuel consumption. Continuous tracking of
behaviour of driver will involve feedback from fleet manager and hence this
will assist driver to further increase the fuel efficiency and also be a safe
driver. This will also assure that comfort level of customers will drastically


All the above are possible through Car
travelling data recorder or Car Black Box which plays an important role in
preventing fatigue driving, over speed and motoring offences, restricting the
driver’s malpractice, analyzing the accident, enforcing traffic management and
transportation, as well as ensuring driving safety of the car.



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