A Review on Voice Control Robotic Car
Aditya Garje*1, Kiran Tamboli*2, Komal Shitole*3, Ritesh Patil*4
Mrs. S.D. Bhirud*5
*1,2,3,4Student, Artificial Intelligence & Machine Learning,
Progressive Education Society’s, Modern College of Engineering,
Shivajinagar, Pune-05.
*5Assistant Professor, Department of Artificial Intelligence & Machine Learning,
Modern College of Engineering, Shivajinagar, Pune-05.
Abstract:
The integration of automation into automotive systems represents a transformative leap in vehicle technology, aimed at improving safety, efficiency, and user experience. This paper provides a comprehensive review of the software components that enable automation in modern vehicles. At the core of automotive automation is the software architecture, which includes the development of advanced algorithms for sensor fusion, perception, path planning, and control systems.
Automation relies heavily on data from various sensors, such as LiDAR, radar, cameras, and GPS, which are processed through machine learning and artificial intelligence algorithms for real-time decision-making. The software stack must ensure accurate object detection, lane-keeping, obstacle avoidance, and adaptive cruise control, all while maintaining a high level of reliability and safety. Furthermore, the software enables vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, contributing to smarter traffic systems and cooperative driving.
Additionally, cloud-based software systems support over-the-air updates and continuous learning, allowing the vehicle's automation capabilities to improve over time based on real-world data and experiences. The paper explores middleware solutions and communication protocols that facilitate the seamless integration of hardware and software components, ensuring both stability and scalability.
By reviewing these software-driven advancements, this paper highlights the critical role of software in shaping the future of automotive automation, addressing challenges such as cybersecurity, system integration, and real-time decision-making, while paving the way for fully autonomous vehicles.
Keywords:
Autonomous Vehicles, Advanced Driver Assistance Systems (ADAS), Simultaneous Localization and Mapping (SLAM), Human-Machine Interface (HMI), Machine Learning, Artificial Intelligence, Computer Vision.
