TY  - JOUR
PY  - 2022//
TI  - Cognitive risk-assessment and decision-making framework for increasing in-vehicle intelligence
JO  - Journal of Sensor and Actuator Networks
A1  - Dimitrakopoulos, George
A1  - Politi, Elena
A1  - Karathanasopoulou, Konstantina
A1  - Panagiotopoulos, Elias
A1  - Zographos, Theodore
SP  - e72
EP  - e72
VL  - 11
IS  - 4
N2  - The key challenge for future automated driving systems is the need to imitate the intelligence and ability of human drivers, both in terms of driving agility, as well as in their intuitive understanding of the surroundings and dynamics of the vehicle. In this paper a model that utilizes data from different sources coming from vehicular sensor networks is presented. The data is processed in an intelligent manner while integrating knowledge and experience associated with potential and any decision. Moreover, the appropriate directives for the safety of the vehicle as well as alerts in case of upcoming emergencies are provided to the driver. The innovation lies in attributing human-like cognitive capabilities--non-causal reasoning, predictive decision-making, and learning--integrated into the processes for perception and decision-making in safety-critical autonomous use cases. The overall approach is described and formulated, while a heuristic function is proposed for assisting the driver in reaching the appropriate decisions. Comprehensive results from our experiments showcase its efficiency, simplicity, and scalability.<p />  <p>Language: en</p>
LA  - en
SN  - 2224-2708
UR  - http://dx.doi.org/10.3390/jsan11040072
ID  - ref1
ER  -