|Title:||Data-driven actors modelling for road transportation|
|Subject:||Computer science,Embedded systems|
Road transportation is a complex system and poses challenges in safe transportation due to dynamic environments changes, where multiple actors are involved. It has been reported that 90% of traffic crashes are caused due to driver risky behaviour. To improve the traffic safety it is not enough to analyse only the risky behaviour of the driver but also a collective behaviour analysis of several actors such as two wheelers, pedestrians i.e., the road users. It also requires analysing interaction between these users and safety related measures.
To understand the dynamic of road users and road safety the work focuses on four actors that are:
1. Vehicle driver
The project aims for a systemic literature review to understand and identify parameters, metrics and indices for all actors. The review will also cover the interaction between different actors and other measures for both safe road usage and road crashes.
Vehicle driving requires a high degree of concentration and it includes various dynamic and complex activities. Through cognition process, a person understands the information about an external object or phenomenon in response to the influence of acquired knowledge, memory, and experience. Among many actions driving depends on the vehicle's state e.g., the speed of the vehicle, lateral position. In addition, driving behaviour signals are obtained from vehicular data, which represent "longitudinal control" or "lateral-control" action.
Hence the second aim of this project is to investigate and implement models for different driving events using driving behaviour signals. Here machine-learning algorithms should include in the model development to find patterns for different driving behaviours.
The project work is subdivided as follows:
1. Literature study and state-of-the-art
This task requires a systematic literature review to identify the parameters, metrics and indices for all actors that can be used modelling actors' behaviour in road transport environment. Student requires presenting an analytical summary of the state-of-the-art based on the literature study.
This task only involves analysing the drivers' vehicular data of various driving events for a given dataset. Student also requires developing an approach using machine-learning algorithms for detection of different driving patterns based on the driving events and vehicular dataset.
Student should evaluate the proposed approach and learning algorithm for detecting driving patterns.
4. Report writing
It is expected that student provide a report as a completion of the project work. Report consists of background, problem formulation, state-of-the-art, methods, evaluation, and discussion.
|IDT supervisors:||Mobyen Uddin Ahmed|