ADAS Advance Driver Assistance Using Nvidia Jetson Nano

Advance Driver Assistance Systems are canny frameworks that dwell inside the vehicle and help the primary driver in an assortment of ways. These frameworks might be utilized to give fundamental data about traffic, conclusion and blockage of streets ahead, clog levels, recommended courses to keep awa

Project Title

ADAS Advance Driver Assistance Using Nvidia Jetson Nano

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Advance Driver Assistance Systems are canny frameworks that dwell inside the vehicle and help the primary driver in an assortment of ways. These frameworks might be utilized to give fundamental data about traffic, conclusion and blockage of streets ahead, clog levels, recommended courses to keep away from blockage and so forth These frameworks may likewise be utilized to pass judgment on the weakness and interruption of the human driver and consequently make prudent alarms or to evaluate the driving exhibition and make ideas with respect to something similar. These frameworks can assume control over the control from the human on evaluating any danger, perform simple errands (like journey control) or troublesome moves (like surpassing and stopping). The best benefit of utilizing the help frameworks is that they empower correspondence between various vehicles, vehicle foundation frameworks and transportation the executives habitats. This empowers trade of data for better vision, restriction, arranging and decision making of the vehicles.Advanced driver assistance system (ADAS) were first being used in 1950s.This is the technology that assist drivers in driving and parking functions.This use the automated technology, such as different sensors and cameras, to detect the nearby obstacles or driver errors, then respond accordingly.ADAS are developed to automate and enhance the vehicular communication.A safe human-machine interface.Advanced driver assistance system (ADAS) were first being used in 1950s.This is the technology that assist drivers in driving and parking functions.This use the automated technology, such as different sensors and cameras, to detect the nearby obstacles or driver errors, then respond accordingly.ADAS are developed to automate and enhance the vehicular communication.A safe human-machine interface.

Project Objectives

Objective

1.Forward collision warning with forward vehicles and pedestrians.

2.Lane analysis and lane departure warning.

3.Sign detection for maximum speed limit signs and over speed warning.

Project Implementation Method

1. The purpose of this project is to design a complete advance driver assistance system.
2. Targeting old and low-cars that are not equipped with, or lack of driver assistance functions.
3. Provide early warning to the system in case of dangerous condition and helps to drive safe. It make driving safe by scanning the roads ahead that helps you to avoid hazards.
4. This can be retrofitted to any vehicle to make driving safe.
5. Main aim regarding the project is to make easily approachable.

Benefits of the Project

Jetson Nano supports high-resolution sensors, can process many sensors in parallel and can run multiple modern neural networks on each sensor stream. It also supports many popular AI frameworks, making it easy for developers to integrate their preferred models and frameworks into the product.The NVIDIA Jetson Nano Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts.Even if used as a standard SBC - the Nano is a great deal (the Shield is even better a deal for that though imo) compared to many other boards that cost the same or more. The benefit with the Nano is the access to the Jetson Package and a platform to learn and test the Cuda software.NVIDIA® Jetson Nano™ lets you bring incredible new capabilities to millions of small, power-efficient AI systems. It opens new worlds of embedded IoT applications, including entry-level Network Video Recorders (NVRs), home robots, and intelligent gateways with full analytics capabilities.In terms of GPU, the Jetson Nano wins because of their 128- core Maxwell GPU @ 921 Mhz. The Raspberry Pi 4 GPU is weaker compared to the Jetson Nano. In the case of CPU, the Raspberry uses the latest and best CPU, the Quad-core ARM cortex-A72 64-bit @ 1.5 GHz

Technical Details of Final Deliverable

In the past, companies have been constrained by the challenges of size, power, cost and AI compute density. The Jetson Nano module brings to life a new world of embedded applications, including network video recorders, home robots and intelligent gateways with full analytics capabilities. It is designed to reduce overall development time and bring products to market faster by reducing the time spent in hardware design, test and verification of a complex, robust, power-efficient AI system.

The design comes complete with power management, clocking, memory and fully accessible input/outputs. Because the AI workloads are entirely software defined, companies can update performance and capabilities even after the system has been deployed.

“Cisco Collaboration is on a mission to connect everyone, everywhere for rich and immersive meetings,” said Sandeep Mehra, vice president and general manager for Webex Devices at Cisco. “Our work with NVIDIA and use of the Jetson family lineup is key to our success. We’re able to drive new experiences that enable people to work better, thanks to the Jetson platform’s advanced AI at the edge capabilities.”

To help customers easily move AI and machine learning workloads to the edge, NVIDIA worked with Amazon Web Services to qualify AWS Internet of Things Greengrass to run optimally with Jetson-powered devices such as Jetson Nano.

Key features of Jetson Nano include:

• GPU: 128-core NVIDIA Maxwell™ architecture-based GPU
• CPU: Quad-core ARM® A57
• Video: 4K @ 30 fps (H.264/H.265) / 4K @ 60 fps (H.264/H.265) encode and decode
• Camera: MIPI CSI-2 DPHY lanes, 12x (Module) and 1x (Developer Kit)
• Memory: 4 GB 64-bit LPDDR4; 25.6 gigabytes/second
• Connectivity: Gigabit Ethernet
• OS Support: Linux for Tegra®
• Module Size: 70mm x 45mm
• Developer Kit Size: 100mm x 80mm

Final Deliverable of the Project

Hardware System

Core Industry

Transportation

Other Industries

Others

Core Technology

Artificial Intelligence(AI)

Other Technologies

Internet of Things (IoT)

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com