Behavioural Pattern Analysis for Safer Driving
Arpit Kumar Rai September 14, 2022
1 Introduction
In recent years, travelling by car has become one of the most preferred forms of transport, thanks to cheaper car models, growing general income and government investments in improving road con- nectivity. But what comes along with it is an unprecedented increase in the number of car accidents and the deaths associated with them. According to sources1, passenger vehicles are by far the most dangerous of the transportation options, with a death rate (per 100,000,000 passenger miles) over 10 times higher than buses, 17 times higher than for passenger trains and 1623 time higher than airlines.
Huge investments are made every year to develop technologies and make standards ensuring safety of passengers upon accidents. But what remains relatively untouched is to prevent accidents in the first place. As of now the standard features aiming to achieve safe driving include high speed alerts, seat-belt alerts, anti-lock brakes, electronic stability control etc. All these features are general purpose and do not take into account the person driving the car.
Thus we propose a feature which is more driver-oriented and employs advanced machine learning algorithms to learn the driving patterns of a person and alerting the driver if some unnatural be- haviour is detected. We hypothesise that this feature can reduce a majority of accidents caused due to distractions, drowsy driving or driving under the influence of drugs or alcohol.
2 Methodology
We propose a machine learning empowered system in automobiles which tracks the behavioural pat- terns of the driver and generates alerts with the aim of minimising road accidents caused due to human fault.
According to sources2, over 75% of the road accidents involve humans at fault. Thus our algorithm targets the major cause of such accidents and tries to take direct measures to minimise the same.
One of the major challenges in developing such an algorithm is the unprecise nature of the problem.
Driving in itself is a complicated task requiring a wide range of information/parameters to consider and hence to model. Just coming up with these parameters for accurate modelling can be a challenging task.
Thus we plan on making an exhaustive list of these parameters including but not limited to age of driver, type of car, type of road, weather conditions etc to come up with a working model. Further we plan to investigate this matter in greater detail with the assistance of professional drivers and car experts.
With the parameters at hand we plan to implement a two way learned model which will first be pre- trained on the general public (the best way in which this set of people can be selected is still under thought). Secondly, we will employ several state-of-the-art machine learning models to further train the algorithm to monitor the driving technique and behaviour of a particular person (maybe the car owner or a driver). We hypothesise that this way of training can better model the driving of a person and generate precise alerts.
The scope of intervention of the algorithm can further be modified and in adverse cases the algorithm may be allowed to perform emergency tasks such as applying brakes. As a separate task, we also plan to train another model which can detect car crashes and inform the relevant authorities thus ensuring timely medical and fire-control services.
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3 Resources and Budget
Item Price/Salary Quantity Cost
Computer System 250000 2 500000
GPU 200000 4 800000
Embedded system and motherboard 50000 - -
Research Team 50000/month - -
Student stipend 12000/month 4 48000/month
Overheads 50000/month - -
4 Evaluation
We plan to deliver the working algorithm at the end of the development period. We also plan to certify the algorithm by various government/private driving authorities as well as a team of professional drivers and medical agencies. A successful certification by these authorities marks the completion of the project.
Timeline
Figure 1: Timeline References:
1https://injuryfacts.nsc.org/home-and-community/safety-topics/deaths-by-transportation-mode
2https://prsindia.org/policy/vital-stats/overview-road-accidents-india
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