Computational–Intelligence–Based Safety Systems for Ground Vehicles

Speaker: Andrei Aksjonov

Robotic Seminar Series. First Session – 14th February 2020, 13:30-14:30, room 2103, TUAS Building, Aalto University.


Traffic accidents have a terrible impact on societal, environmental, and economical norms all over the world. To raise the safety systems of ground vehicles to a new level, vehicle–environment–driver interaction is of high importance. Computational intelligence algorithms (i.e. artificial neural networks, fuzzy system, etc.) open a great opportunity for managing and creating synergy between complex stochastic plants and human factor. These algorithms provide capability to approximate qualitative aspect of human reasoning and decision–making process in human-machine-environment systems. Hence, the motivation of this research is to improve such essential and comprehensive ground vehicle safety systems, as antilock braking and driver distraction detection and evaluation, applying the computational intelligence approaches. On one hand, the antilock braking system complexity is caused by many nonlinearities, such as the hydraulic brake circuit dynamics and the tire–road adhesion coefficient characteristic, which considerably depends on road states (i.e. weather conditions and quality of the surface, temperature, etc.), as well as on vehicle conditions. On the other hand, driver distraction hardship is resonated with human factor, which is unpredictable and very comprehensive to deal with. The proposed blended antilock braking system is managed by a universal fuzzy logic unit, which serves as a road surface identifier and a controller simultaneously. Furthermore, the developed driver distraction detection and evaluation method results in continues level of distraction estimation, what allows for precise measurement of its influence on the secure vehicle control. The hardware– and driver–in–the–loop experimental results are presented.

  • Aksjonov, A., Augsburg, K., & Vodovozov, V. (2016). Design and Simulation of the Robust ABS and ESP Fuzzy Logic Controller on the Complex Braking Maneuvers. Applied Sciences, 6(12), 1–18. doi: 10.3390/app6120382
  • Aksjonov, A., Nedoma, P., Vodovozov, V., Petlenkov, E., & Herrmann, M. (2018). Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic. IEEE Transactions on Intelligent Transportation Systems, Early Access. doi: 10.1109/TITS.2018.2857222
  • Aksjonov, A., Vodovozov, V., Augsburg, K., & Petlenkov, E. (2018). Design of Regenerative Anti−Lock Braking System Controller for 4 In−Wheel−Motor Drive Electric Vehicle with Road Surface Estimation. International Journal of Automotive Technology, 19(4), 727−742. doi: 10.1007/s12239–018–0070–8
  • Aksjonov, A., Vodovozov, V., Augsburg, K., & Petlenkov, E. (2020). Blended Antilock Braking System Control Method for All–Wheel Drive Electric Sport Utility Vehicle. ELECTRIMACS 2019: Selected Papers – Volume 1. Springer International Publishing. doi: 10.1007/978-3-030-37161-6.
  • Aksjonov, A., Ricciardi, V., Vodovozov, V., Augsburg, K., & Petlenkov, E. (2020) Hardware–in–the–Loop Test of a Fuzzy–Logic–Based Control Method for Antilock Braking System on All–Wheel Drive Electric Vehicle. IEEE Transactions on Fuzzy Systems, Early Access. doi: 10.1109/TFUZZ.2020.2965868.