Abstract
Multi-object detection and multi-object-tracking in diverse driving situations is the main challenge in autonomous vehicles. Vehicle manufacturers and research organizations are addressing this problem, with multiple sensors such as camera, LiDAR, RADAR, ultrasonic-sensors, GPS, and Vehicle-to-Everything-technology. Deep Neural Networks (DNN) are playing a predominant role to solve this. Fusing the sensing modalities with DNN will be the leading solution to this challenge. This paper evaluates the state-of-the-art techniques that address this challenge, with three primary sensors camera, LiDAR, and RADAR with DNN, and fusion of sensor data with DNN. The analysis shows that there exists an excellent potential to design a more optimized solution to address this challenge. This work proposes a perception model for autonomous vehicles.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
