Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Abstract

Autonomous vehicles operate in real time traffic scenarios and aim to reach their destination from their source in the most efficient manner possible. Research in mobile robotics provides a variety of sophisticated means with which to plan the path of these vehicles. Conversely professional human drivers usually drive using instinctive means, which enables them to reach their goal almost optimally whilst still obeying all traffic laws. In this paper we propose the use of fuzzy logic for novel motion planning. The planner is generated using an evolutionary algorithm which resembles the learning stage of professional drivers. Whether to overtake or not, is a decision which affects one's driving and the decision is made using some deliberation. We further extend the approach to perform decision making regarding overtaking for all vehicles. Further we coordinate the motion of the vehicles at a traffic crossing to avoid any potential jam or collision. Experimental results prove that by using this approach we have been able to make the vehicles move in an optimal manner in a variety of scenarios.

An attempt to determine the impact of the implementation of autonomous vehicles on a larger scale on the planning of city transport systems

“…Such a movement is referred to as an organized movement. However, several nations have chaotic traffic patterns where cars can drive in and out of lanes (Kala & Warwick, 2015). When the vehicles are significantly dissimilar in size and speed, traffic flow can be greatly improved in such circumstances.…”

“…Because of the size disparity, more cars may share a lane, increasing traffic bandwidth. Due to the difference in speeds, it is urgently necessary for a very fast car to pass a very slow car on the road (Kala & Warwick, 2015). This can be facilitated if the slow car can change across lanes to allow for an earlier overtake.…”

Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

“…Concerning calculation methods, which are used in navigation, we will focus on methods with the ability to process inaccurate data from sensors because this problem is particularly significant in this application area [44]. Besides conventional fuzzy controllers, for example, [45], where an evolutionary algorithm is used for adjusting rules of such a fuzzy controller, also FCMs have found their growing use in recent years. FCM proposed in [46] is intended for wheeled robots and utilizes data from sensors mounted onboard.…”

Applications of Soft Computing in Intelligent Transportation Systems

“…Motion planning has been another topic where fuzzy logic has been applied. For instance, in [37], Kala et al implemented decision making over autonomous vehicle maneuvering. Pedals control by fuzzy logic has been also tackled in several works, as [51] or [34].…”