Evaluation of estimation approaches on the quality and robustness of\n collision warning system

Vehicle safety is one of the most challenging aspect of future-generation\nautonomous and semi-autonomous vehicles. Collision warning systems (CCWs), as a\nproposed solution framework, can be relied as the main structure to address the\nissues in this area. In this framework, information plays a very important\nrole. Each vehicle has access to its own information immediately. However,\nanother vehicle information is available through a wireless communication. Data\nloss is very common issue for such communication approach. As a consequence,\nCCW would suffer from providing late or false detection awareness. Robust\nestimation of lost data is of this paper interest which its goal is to\nreconstruct or estimate lost network data from previous available or estimated\ndata as close to actual values as possible under different rate of lost. In\nthis paper, we will investigate and evaluate three different algorithms\nincluding constant velocity, constant acceleration and Kalman estimator for\nthis purpose. We make a comparison between their performance which reveals the\nability of them in term of accuracy and robustness for estimation and\nprediction based on previous samples which at the end affects the quality of\nCCW in awareness generation.\n