A Detailed Study on Obstacle Detection and Avoidance Techniques for On-road Vehicles
Obstacle detection
Autonomous vehicles are vehicles that can maneuver themselves without any human intervention. These autonomous vehicles must be capable of reciprocating every human activity on any given challenge or obstacle without compromising the safety of the passengers and innocent bystanders. One of the most common obstacles seen on the roads are Pedestrians jaywalking, Potholes, and animals occasionally in countries in the eastern hemisphere. Primarily self-driving vehicles make use of sensors such as LiDARs, SONARs, and Accelerometers in-order to navigate through the roads effortlessly. These sensors can detect the presence of an obstacle on the road and a control system can act accordingly. Sensor data are accurate and precise and not susceptible to the weather or environment. Other approaches involve using computer vision where a camera is attached to a car that can detect obstacles by using popular machine learning and deep learning approaches. These methodologies are preferred as they are cost-effective compared to incorporating sensors in vehicles. Some approaches use image processing techniques to detect the presence of obstacles. The methods are used to reduce the time of data collection and model training.
Obstacle Avoidance
Once the obstacles have been detected, the next step involves using steps and methodologies to circumvent the obstacles to avoid colliding with them. One of the most common obstacle avoidance techniques is by using Fuzzy logic controllers that take in input from the obstacle detection system and makes a plan to avoid the obstacle. Fuzzy logic involves rules to avoid obstacles, these rules address various ranges of variables thus making it a robust and efficient system. Fuzzy logic is preferred as it can formalize the controller for simple tasks such as driving on an empty road or complex tasks such as driving through a heavily crowded lane or when parking the vehicle. Some studies used early braking as a method to avoid colliding with obstacles. These methods would measure the distance and apply the brakes accordingly. Other methods use machine learning and deep learning techniques to avoid obstacles. Finally, VANET architectures have also been explored where vehicles could send and receive data amongst each other.
