This is a Road Accident Manager and Crash Investigation Pattern Analyser.
Road accidents and injuries occur because of human, vehicle or infrastructure faults or sometimes a combination of all three. Various parameters that affect the crash investigation statistics generate hidden patterns that can be extracted to find the various unknown risk factors behind fatal accidents and predict accident-prone areas. Existing systems lack the ability to investigate because of shortage of non-erroneous post crash data.
- Crash-proof Hardware : Capable of recording the real time accident data which enhances the authenticity of the model over time.
- Multi-featured Android App : Generates a heatmap displaying accident prone areas in your way and alerts suitably.
- Secured Investigation Portal : Gives secure access to the collected data via blockchain to authorities.
- Least cost alternatives help in better reachability of the product among the masses.
- Less network usability ensures that the product is usable in scarce circumstances.
- The crash-proof setup helps to achieve cost effectiveness.
- Sustainability is achieved with less hardware requirement and recyclable products.
The EDR is a concrete open system which can be viewed solely in terms of its input and output reactions. All the circuitry functioning takes place inside this closed crash proof container. Main computing is performed by NodeMCU and RaspberryPi connected with components such as : Sensors, GPS module, Cameras.
- Watch various environmental, mechanical and physical factors responsible for any mishap.
- Help to suggest a better convenient speed for the driver and subsequently, a brake failure detection.
The memory unit stores the real time data filtered by the RPi. Only the necessary information is transferred to the Cloud after every specific time interval ( say 5 minutes ). This data is in the form of log files which are of very small size ( ~ 100 KB), thereby, reducing the heavy internet connection requirement and maintaining storage efficiency.
In order to provide this Cloud communication, multiple docker containers on a single Linux machine are used. They perform some dedicated tasks, for instance:
- Used to run the application, ML model, handling http requests and to keep a record of all the data input.
- Used for backup purpose, in case of a failed network request, the data is still available.
To provide a secure communication only to the authenticated users, Port Forwarding is used. This ensures that no mismanagement/ manipulation of data.
The collected data goes through a series of processing:
- All the nearby objects and surroundings (including number plates) from the 1 minute pre-crash video will be extracted in the form of labelled snapshots.
- These snapshots will be generated by a tensorflow model on the NodeMCU itself.
The data is divided into a total of 30 attributes that focus on criteria such as accident-specific, driver-specific and circumstance-specific attributes. The Clusters formed are:
- Traffic cluster-low high traffic
- Time of accident-morning, afternoon, night
- Age of driver
- Accident month
- Weather condition
- Type of accidents - rash driving, drunken driving, vehicle skidding, overriding
- Speed of vehicles at the time of accidents
Next, hidden patterns and facts from these clusters are extracted. These hidden patterns give analysis on various unknown risk factors for fatal accidents and predict accident-prone areas.
- On the start of the journey, the app prompts the destination and displays the risks of all the possible routes accordingly.
- Once a particular route is chosen, it displays all the accident prone areas, potholes etc that the driver will encounter on the way and displays safe permissible speed accordingly.
- OnRoad also provides emergency buttons (for medical help and police) which will contact the nearest police station or ambulance.
- A separate web portal provides statistics to the authorities for investigation.
- The ML predicted details regarding all accidents in a particular region are available.
- Density histograms for visualizing region-wise results are shown to help identify the risk of the accident immediately.
Experience OnRoad in your own local environment with our step by step guide.
- Coming from a geographically diverse country like India, we plan to take into account various landforms while implementing our proposed system. Different terrains like mountains, coastal areas etc, call for various types of natural calamities. Our system would be capable of identifying different patterns and changes caused due to these natural or physical calamities. Accordingly, the plan of action would be modified in the ML database.
- Socially, this project creates a wide impact, by making investigations accurate by providing much more reliable proofs and by making people aware of the potential threats that could be encountered on their way. It provides for an all time road companion that will make your journeys more secure.
© Team Celestia