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Connected Cars: Computational Models for Time-Critical Safety Applications

Primary supervisor

Adel Nadjaran Toosi


Connected vehicles need to be aware of their surrounding environments. This is impossible without being dependent on many sensory inputs. Sensor data is continually collected and analysed, in real-time in order to perform time-critical and delay-sensitive actions. There are two major challenges 1) limited computational resources (processing power and memory) on cars, 2) transfer of large sensory data to the cloud may is not feasible. In this project we aim at building scheduling and task offloading techniques to share the computation tasks between the vehicles and cloud considering the computational resources of the vehicles, Quality of Service (QoS) requirements of the applications (e.g., required response time), and available bandwidth.

Student cohort

Single Semester
Double Semester


Aim: The project aims to self diagnose car dynamics in real-time using i.e onboard diagnostics, GPS (spark fun geolocation tracking), Camera and other sensors.  It monitors car speed, acceleration, fuel consumption, etc. The project also aims to track car's real-time location and also wants to track the live objects that come in the periphery of the car while driving i.e real-time object detection of objects like - Signboards, Animals, Traffic lights, buildings etc. We would like to build a prototype using Raspberry Pi 3 and other sensor devices. We use a Raspberry Pi 3 as the core of the system connected to all sensors and collect data from sensors. Then it will use its cellular connection to send data to the cloud.



List of devices that will be used:

ELM327 Bluetooth OBD2 OBDII
Veepeak OBDCheck BLE+ Bluetooth 4.0 OBD2
Raspberry Pi 3 Model B+
SparkFun GPS-RTK Dead Reckoning pHAT for Raspberry Pi
HighPi Raspberry Pi B+/2/3 Case - Black
4G / 3G / GNSS HAT Module for Raspberry Pi
Multiprotocol Development Tools DWM1001 Development Board
Raspberry Pi Zero W Camera Kit

Required knowledge


Solid programming skills in Python
Linux shell scripting skills 
Basic knowledge of distributed systems and computer networks
Basic software engineering skills


Background in Machine Learning, Optimisation, and Data Structures
Experiences with Raspberry Pis
Basic cloud computing skills (e.g. AWS)