Lane And Curve Detection using Deep Learning for Driving Assistance System

Project Title

Lane And Curve Detection using Deep Learning for Driving Assistance System

Project Area of Specialization Artificial IntelligenceProject Summary

We will design an self driving car. That is able to detect the lanes and curves in road once the car is intelligent enough to detect and take desion of either to move left right or straigh.The car is intelligent enough to that it will take decision like where to aplly brake and automatically maintaines the distance on the road.The development of automation and electrification, autonomous robotics and
vehicles has a wide range of use in the space science and self-driving cars. An
important part in the autonomous vehicles is navigation system. In the past
decades using vision based systems guidance system was become more popular.
By taking the tire and road interaction from the vehicles automatically the
terrain has to be classified these type of technique was using in the rovers and
path finder now it came to the automobiles.In this project a deep learning
technique is used to detect the curved path in autonomous vehicles. In this
paper a customized lane detection algorithm was implemented to detect the
curvature of the lane. A ground truth labelling tool box for deep learning is
used to detect the curved path in autonomous vehicle. By mapping point to
point in each frame 80-90(percent) computing efficiency and accuracy is achieved
in detecting path.The development of automation and electrification, autonomous robotics and
vehicles has a wide range of use in the space science and self-driving cars. An
important part in the autonomous vehicles is navigation system. In the past
decades using vision based systems guidance system was become more popular.
By taking the tire and road interaction from the vehicles automatically the
terrain has to be classified these type of technique was using in the rovers and
path finder now it came to the automobiles.In this project a deep learning
technique is used to detect the curved path in autonomous vehicles. In this
paper a customized lane detection algorithm was implemented to detect the
curvature of the lane. A ground truth labelling tool box for deep learning is
used to detect the curved path in autonomous vehicle. By mapping point to
point in each frame 80-90(percent) computing efficiency and accuracy is achieved
in detecting path.

Project Objectives

? Make Drive Easy for Everyone.
? Reduce Chances of Accident.
? Providing Safe and Comfortable Drive.
? Charm of Self Driving.
? Drive Without any Fear.
? No Need of Expert Driver.
? Reduces Traffic Problems.

Project Implementation Method

The most important part is the curve line detection part. This method should detect a straight or a curve line in the far-field of view. Image data (white points in the far-field of view) include uncertainties and noise generated during capturing and processing steps. Therefore, as a robust estimator against these irregularities, a Kalman filter was adopted to form an observer [22]. First of all, we need to define the equation of the curve line, which is a non-linear equation. For the curve line, the best-fit equations are the parabola equation and the circle equation.

In this part, we consider a curve lane detection algorithm which is based on the Kalman filter and Parabola equation.

Benefits of the Project

The development of the self-driving car is needed for the safety of driver and passenger on the vehicle [1]. Traffic accidents occur for various reasons. The majority of traffic accidents are caused by an improper speed on the road turning or unexpected lane changes when avoiding an obstacle [2]. Some modern cars are already equipped with the emergency braking system, collision warning system, lane-keeping assist system, adaptive cruise control. These systems could be used to help avert traffic accidents when driver is distracted or lost control.

The two most important parts of advanced driver assistance systems are a collision avoidance system and a Lane keeping assist system, which could help to reduce the number of traffic accidents. A fundamental technique for effective collision avoidance and lane-keeping is a robust lane detection method [3]. Especially that method should detect a straight or a curve lane in the far-field of view. A car moving at a given speed will spend a certain time to stop or reduce speed while keeping stability. This means it is necessary to detect road lane in the near field as well as in far-field of view.

Technical Details of Final Deliverable

Since 2012, society has seen drastic improvements in the fields of automated/autonomous data analysis, informatics, and deep learning (defined later). The advancements stem from gains in widespread digital data, computing power, and algorithms applied to machine-learning (ML) and artificial intelligence (AI) systems. Here, we distinguish the term ML as obtaining a computed model of complex non-linear relationships or complex patterns within data (usually beyond human capability or established physics to define), and AI as the framework for making machine-based decisions and actions using ML tools and analyses. Both of these are necessary but not sufficient steps for attaining autonomous systems. Autonomy requires at least three concurrently operating technologies: (i) perception or sensing a field of information and making analyses (i.e., ML); (ii) predicting or forecasting how the sensed field will evolve or change over time; and (iii) establishing a policy or decision basis for a machine (robot) to take unsupervised action based on (i) and (ii). We note that item (ii) in the aforementioned list is not often discussed with respect to ML since the technical essence of item (ii) resides within the realm of control theory/control system engineering.

Final Deliverable of the Project Software SystemCore Industry TransportationOther Industries Others Core Technology Artificial Intelligence(AI)Other Technologies RoboticsSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	70000
Lane and Curve Detection using deep learning for driving assistance	Equipment	1	70000	70000