@conference {450,
	title = {{A LiDAR Error Model for Cooperative Driving Simulations}},
	booktitle = {IEEE Vehicular Network Conference},
	year = {2018},
	month = {12/2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Taipei, Taiwan},
	abstract = {<p>Cooperative driving and vehicular network simulations have done huge steps toward high realism.\&nbsp;They have become essential tools for performance evaluation of any kind of vehicular networking application.\&nbsp;Yet, cooperative vehicular applications will not be built on top of wireless networking alone, but rather fusing together different data sources including sensors like radars, LiDARs, or cameras.\&nbsp;So far, these sensors have been assumed to be ideal, i.e., without any measurement error.\&nbsp;This paper analyzes a set of estimated distance traces obtained with a LiDAR\&nbsp;sensor and develops a stochastic error model that can be used in cooperative driving simulations.\&nbsp;After implementing the model within the PLEXE\&nbsp;simulation framework, we show the impact of the model on a set of cooperative driving control algorithms.</p>
},
	keywords = {autonomous vehicles, AV, CAV, LiDAR, self-driving cars, sensor, VNC},
	author = {Michele Segata and Renato Lo Cigno and Rahul Bhadani and Matthew Bunting and Jonathan Sprinkle}
}
