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ROOD

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  • RMIT

Description

In the past few years in Australia, there has been an increase in road traffic and peri-urban. This has resulted in the great injury and deaths of wildlife and motorists. This does not only affect human safety but also animal welfare.
ROOD is a roadkill alert system which aims to mitigate the wild animal-vehicle collision by using IoT, object recognition technology and citizen science.
ROOD includes a dashcam and an add-on device which connected to a database of roadkill hotspots. It can alert the driver via four types of tactile and light effects when they\’re driving in the high-risk area.

Key Features

1

ROOD empowers users to report the roadkill carcass GPS location and picture via the add-on device on the steering wheel and the dashcam.
The GPS location can be used to set up the instant alert to the drivers close by and highlight the permanent high-risk area based on the collision rate. The pictures will be used on machine learning, the machine can learn from the images that drivers recognise as carcasses.
Through learning from drivers, the system can be embedded in the autonomous car in the future which turns every car into a data collector.

2

ROOD is a system that not just design for solving the current situation but also designed for fitting in the future.
Through data collection, analysis to change driver behaviour in order to mitigate the wild animal-vehicle collision effectively.
ROOD requires drivers to participate in the process of recognising carcasses during driving which potentially prevents drowsy driving from happening.
Nowadays, the most effective way to solve the roadkill issue is building the animal bridge or tunnel for animals to find an alternative way to cross the road. However, for road authority and biologist to locate where is the high-risk area requires considerable time and resource. Rood enables citizens to help collect data and keep the data up to date which benefit the road authority and biologist to pin out the area and do the reaction in a short time.

3

Through analysis of precedences, research and interview, there are three main topics involved in the animal-vehicle collision, which are driver behaviour, Australian animals’ behaviours, and the infrastructure around the roadside. The three exploratory research projects looked into these three territories in order to seek the potential solution to this problem.
Through a series of exploratory design projects, it is found that changing animal behaviour is unethical and unpredictable which indicates the outcome of focusing on changing driver’s behaviour. Therefore, I looked into the territory of changing driver’s behaviour by making them aware while driving in high-risk areas.

4

Rood implements circular design thinking, using existing technology (IoT, Machine learning) to mitigate the issue but it also constantly improving itself via machine learning in order to prepare for future technology. (autonomous car)

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