Abstract The project intends to increase vehicle operator awareness with the integration of a Low-Cost Driving Assistance system in older car models. Situational awareness during driving significantly reduces the number of road traffic accidents, as proved by literature. A 4-wheel mobile robot is used as the plant representing a vehicle for easier and rapid prototyping. The plant is controlled by a Raspberry Pi3 to achieve the desired control choice as well as do all required computational processes. Image processing using a retrained Resnet50 neural network is adopted for road traffic signs. A 54% accuracy rate for image recognition is recorded. The wheeled mobile robot is successfully modeled and deemed unstable while the circuit is simulated and works as expected.
Mangezi, S (2021). LOW COST VEHICLE DRIVING ASSISTANCE SYSTEM. Afribary. Retrieved from https://afribary.com/works/low-cost-vehicle-driving-assistance-system
Mangezi, Stewart "LOW COST VEHICLE DRIVING ASSISTANCE SYSTEM" Afribary. Afribary, 21 Mar. 2021, https://afribary.com/works/low-cost-vehicle-driving-assistance-system. Accessed 23 Nov. 2024.
Mangezi, Stewart . "LOW COST VEHICLE DRIVING ASSISTANCE SYSTEM". Afribary, Afribary, 21 Mar. 2021. Web. 23 Nov. 2024. < https://afribary.com/works/low-cost-vehicle-driving-assistance-system >.
Mangezi, Stewart . "LOW COST VEHICLE DRIVING ASSISTANCE SYSTEM" Afribary (2021). Accessed November 23, 2024. https://afribary.com/works/low-cost-vehicle-driving-assistance-system