Hydroplaning poses a significant risk to road safety, as a water wedge between the tires and the road reduces the vehicle’s responsiveness to driver inputs. This phenomenon is influenced by factors such as vehicle speed, water depth, and tire wear, directly impacting parameters like cornering stiffness, relaxation length, and friction coefficient. This study evaluates a control logic designed to assist drivers during hydroplaning. A novel tire model was developed and integrated into a 14-degree-of-freedom vehicle model to simulate hydroplaning effects, while an Advanced Driver-Assistance System (ADAS) was designed to enhance vehicle control. A dynamic driving simulator was used to test driver interactions with the proposed control system and assess its effectiveness. The ADAS was evaluated in double-lane-change and high-speed turn scenarios, where drivers of varying experience levels were asked to complete the maneuver at different speeds, both with and without the control system’s assistance. The control strategy helped the drivers to complete the maneuvers, reducing their effort on the steering wheel. In conclusion, this study provided valuable insights into how everyday drivers manage hydroplaning, highlighting ADAS’s potential to improve vehicle control and safety in such conditions.
AboeòsaoudMTahaAAAbo ElazmM, et al. (2025) A dynamic hydroplaning study: A fluid-structure interaction model for road vehicle tire analysis. SAE International of Passenger Vechicle Systems18(3): 221–241. https://doi.org/10.4271/15-18-03-0015
2.
AlbertBJ (1968) Tyres and hydroplaning, automotive engineering congress and exposition. SAE International.
3.
ArosioDBraghinFCheliF, et al. (2005) Identification of Pacejka's scaling factors from full-scale experimental tests. Vehicle System Dynamics43(1): 457–474. https://doi.org/10.1080/00423110500229683
4.
BielseFHermangeCTodoroffV, et al. (2022) Experimental investigation of the leading parameters influencing the hydroplaning phenomenon. Vehicle System Dynamics60(11): 2375–2392.
5.
BlandinaGAFassioD, (2020). An active safety system able to counter acquaplaning, integrated with sensorized tires, adas and 5g technology for both human-driven and autonomous vehicles. In: Conference on Sustainable Mobility. SAE International, Sep 2020.
CerezoVGothieMMenissierM, et al. (2010) Hydroplaning speed and infrastructure characteristics. Proceedings of the Institution of Mechanical Engineers - Part J: Journal of Engineering Tribology224(9): 891–898. https://doi.org/10.1243/13506501jet738
8.
ErseusATrigellASDruggeL (2013) Methodology for finding parameters related to path tracking skill applied on a DLC test in a moving base driving simulator. International Journal of Vehicle Autonomous Systems11(1): 1–21.
9.
FichtingerAEdelmannJPlochlM, et al. (2021) Aquaplaning detection using effect-based methods: an approach based on a minimal set of sensors, electronic stability control, and drive torques. IEEE Vehicular Technology Magazine16(3): 20–28. https://doi.org/10.1109/mvt.2021.3085536
10.
FwaTSrirangemSAnupamK, et al. (2009) Effectiveness of tyre-tread patterns in reducing the risk of hydroplaning. Transportation Research Record Journal of the Transportation Research Board2094: 91–102.
11.
GobbiMMastinuGMelziS, et al. (2022) A driving simulator for UN157 homologation activities. In: Proc. of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Houston, TX, USA, 23–26 August 2026.
12.
HartmanBRasteT (2019) Hydroplaning Avoidance – a Holistic System Approach. Klöster.
13.
JalaliKUchidaTMcPheeJ, et al. (2013) Development of an advanced fuzzy active steering controller and a novel method to tune the fuzzy controller. SAE International Journal of Passenger Cars – Electronic and Electrical Systems6(1): 241–254.
14.
JindřichFMotlJMikulecR, et al. (2019) Acquaplaning-preventing device based on blowing a wet road with a stream of air. Transportation Research Proceding44(Suppl 1): 290–296.
15.
KienleRResselWGötzT, et al. (2020) The influence of road surface texture on the skid resistance under wet conditions. Proceedings of the Institution of Mechanical Engineers234(3): 313–319.
16.
MalenskaMPetryFFehrD, et al. (2021) Longitudinal hydroplaning performance of passenger car tyres. Vehicle System Dynamics59(11): 1–18.
17.
MetzLD (2011) Hydroplaning behaviour during steady-state cornering maneuvers. SAE Internation Journal of Materials and Manufacturing4(1): 1068–1079.
18.
MetzLD (2012) Potential for hydroplaning behavior during transient maneuvers. SAE Int.
19.
MetzLD (2014) Simulation of transient maneuver hydroplaning events using HVE. SAE Technical Paper 2014-01- 0122,1: 2014. https://doi.org/10.4271/2014-01-0122
MounceJMBartoskewitzRT (1993) Hydroplaning and roadway tost 10.1016/j.triboint.2025.111579liability. Transportation Research Record.
22.
MuellerJStanleySMartinT, et al. (2014) Driving simulator and scenario effects on driver response. In: Proceedings of the 2014 Industrial and Systems Engineering Research Conference, Canada, 31 May–3 June 2014.
23.
NiskanenAJTuononenAJ (2014) Three 3-axis accelerometers fixed inside the tyre for studying contact patch deformations in wet conditions. Vehicle System Dynamics52(Suppl 1): 287–298.
24.
NiskanenAJTuononenAJ (2015) Accelerometer tyre to estimate the aquaplaning state of the tyre-road contact. In: IEEE Intelligent Vehicles Symposium IV, Seoul, Korea, 28 June–01 July 2015.
25.
PacejkaHB, (2006). Tyre and vehicle dynamics. In: Butterworth-Heinemann, Chapter 4: “Semi-Empirical Tyre Models”, (pag 176-182).
26.
RasteTFrerichs (2016) Study on Active Safety for Acquaplaning Assistance, Advanced Vehicle Control.
27.
SbrosiMD’AlessandroVMelziS, et al. (2012) Phenomenological analysis of hydroplaning through intelligent tyres. Vehicle System Dynamics50(Suppl 1): 3–18.
28.
WongJ (2001) Theory of Ground Vehicles. Wiley.
29.
World Health Organization (2019) Global Status Report on Road Safety.
30.
YuYWangHWuY, et al. (2026) Skid resistance assessment of wet asphalt runways by coupling finite element simulation with real texture evolution data. Tribology International216: 111579. https://doi.org/10.1016/j.triboint.2025.111579
31.
ZhongQWangYZhouX, et al. (2025) Simulation analysis of tire viscous hydroplaning on wet pavement. Proceedings of SPIE - The International Society for Optical Engineering13781, art. no. 1378107: 193. Available at: https://doi.org/10.1117/12.3079170
