A ’Cognitive Driving Framework’ for Collision Avoidance in Autonomous Vehicles
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2015, Vol 6, Issue 5
Abstract
The Cognitive Driving Framework is a novel method for forecasting the future states of a multi-agent system that takes into consideration both the intentions of the agents as well as their beliefs about the environment. This is partic-ularly useful for autonomous vehicles operating in an urban environment. The algorithm maintains a posterior probability distribution over agent intents and beliefs in order to more accurately forecast their future behavior. This allows an agent navigating the environment to recognize dangerous situations earlier and more accurately than competing algorithms, therefore allowing the agent take actions in order to prevent collisions. This paper presents the Cognitive Driving Framework in detail and describes its application to intersection navigation for au-tonomous vehicles. The effects of different parameter choices on the performance of the algorithm are analyzed and experiments are conducted demonstrating the ability of the algorithm to predict and prevent automobile collisions caused by human error in multiple intersection navigation scenarios. The results are compared to the performance of prevailing methods; namely reactionary planning and constant velocity forecasting.
Authors and Affiliations
Alan Hamlet, Carl Crane
Keywords
Related Articles
- EP ID EP89532
- DOI 10.14569/IJACSA.2015.060519
- Views 150
- Downloads 0
A Comparison Study between Data Mining Tools over some Classification Methods
Nowadays, huge amount of data and information are available for everyone, Data can now be stored in many different kinds of databases and information repositories, besides being available on the Internet or in printed f...
Search Manager: A Framework for Hybridizing Different Search Strategies
In the last decade, many of the metaheuristic search methods have been proposed for solving tough optimization problems. Each of these algorithms uses its own learn-by-example mechanism in terms of “movement strategy” to...
Arabic Lexicon Learning to Analyze Sentiment in Microblogs
The study and classifying of opinions distilled from social media is called sentiment analysis. The goal of this study is to build an adaptive sentiment lexicon for Arabic language. Based on those lexicons the sentiments...
Role Term-Based Semantic Similarity Technique for Idea Plagiarism Detection
Most of the text mining systems are based on statistical analysis of term frequency. The statistical analysis of term (phrase or word) frequency captures the importance of the term within a document, but the techniques t...
An IoT based Warehouse Intrusion Detection (E-Perimeter) and Grain Tracking Model for Food Reserve Agency
Zambia’s agricultural sector through Food Reserve Agency (FRA) while still underdeveloped faces many challenges that range from marketing, spoilage, infestations, and theft at site, spillage and storage among others. The...