Data-driven feedback algorithms for automated position identification and environment reconstruction of autonomous vehicles

Abstract

Abstract Independently of the automation level challenging aspects on unmanned vehicle management such as performance optimization processes, adaptive cruise control systems, steering and braking controls, are related to the assessment of the operational functioning degree of relevant responsive procedures. This paper states the problem of determining both the position and the environment of fully autonomous cars for safe and efficient car routing control. An adaptive, closed loop policy is proposed utilizing a minimum number of measurements available by principal car technology, a laser sensor and a smart camera. No additional investment is required on other devices where the costly continuous real time scans can be now reduced. The low computational complexity encourages for embedded designs providing real time responses. Missing or invalid sensor information is estimated by a first approach using neural network programming. Positions of mobile and immobile entities are determined and the related environmental context within the desired vehicle vicinity is precisely reproduced. The performance of the proposed methodology is evaluated through realistic simulation data fed by multiple ground truth recordings. Hence, multiple biases on direct information as generated by professional drivers leading to untruthful and unreliable conclusions are now minimized. A trajectory within Paris central areas and suburbs is considered involving varying traffic conditions and rich road infrastructure. Some first qualitative and quantitative results appraise the accuracy and performance of the proposed methodology while further research targets are discussed.