Enhancing Vehicle Intelligence via Environment Perception and Modeling
Nilesh Kolhe, Aruna Verma
Intelligent vehicles, environment perception and modeling, lane and roaddetection, traffic sign recognition, vehicle tracking and behaviour analysis, scene understanding
Research and development on environmental perception, advanced sensing, and intelligentdriver assistance systems aim at saving human lives. A wealth of research hasbeen dedicated to the development of driver assistance systems and intelligent vehiclesfor safety enhancement. For the purposes of safety, comfort ability, and saving energy,the field of intelligent vehicles has become a major research and development topic inthe world. Many government agencies, academics, and industries invest great amount ofresources on intelligent vehicles, such as Carnegie Mellon University, Stanford University,Cornell University, University of Pennsylvania, Oshkosh Truck Corporation, Peking University,Google, Baidu, and Audi. Autonomous vehicles are expected to play a key role inthe future of urban transportation systems, as they offer potential for additional safety,increased productivity, greater accessibility, better road efficiency, and positive impact onthe environment. Research in autonomous systems has seen dramatic advances in recentyears, due to the increases in available computing power and reduced cost in sensingand computing technologies, resulting in maturing technological readiness level of fullyautonomous vehicles. The objective of this paper is to provide a general overview of therecent developments in the realm of autonomous vehicle software systems. Furthermore, many challenges have been held to test the capability of intelligent vehiclesin a real-world environment. Intelligent vehicles are also called autonomous vehicles,driver-less vehicles, or self-driving vehicles. An intelligent vehicle enables a vehicle tooperate autonomously by perceiving the environment and implementing a responsive action.It comprises four fundamental technologies: environment perception and modeling,localization and map building, path planning and decision-making, and motion control. A special attention is paid to methods for lane and road detection, traffic sign recognition,vehicle tracking, behaviour analysis, and scene understanding. In addition, weprovide information about data sets, common performance analysis, and perspectives onfuture research directions in this area.
Article Details
Unique Paper ID: 147433

Publication Volume & Issue: Volume 5, Issue 8

Page(s): 16 - 20
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Last Date 25 February 2020

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