US 12,214,775 B2
Road surface recognition apparatus, vehicle having the same, and method of controlling vehicle
Ik Jin Um, Busan (KR); Jun Han Kang, Seoul (KR); Jungho Park, Incheon (KR); Man Dong Kim, Hwaseong-si (KR); Chung Choo Chung, Seoul (KR); Seung-Hi Lee, Seongnam-si (KR); Dae Jung Kim, Seoul (KR); and Jin Sung Kim, Seoul (KR)
Assigned to Hyundai Motor Company, Seoul (KR); Kia Corporation, Seoul (KR); and Industry-University Cooperation Foundation Hanyang University (IUCF-HYU), Seoul (KR)
Filed by HYUNDAI MOTOR COMPANY, Seoul (KR); KIA CORPORATION, Seoul (KR); and Industry-University Cooperation Foundation Hanyang University (IUCF-HYU), Seoul (KR)
Filed on Jul. 27, 2021, as Appl. No. 17/386,031.
Claims priority of application No. 10-2020-0116952 (KR), filed on Sep. 11, 2020.
Prior Publication US 2022/0080952 A1, Mar. 17, 2022
Int. Cl. B60W 30/02 (2012.01); B60W 10/06 (2006.01); B60W 10/18 (2012.01); B60W 30/18 (2012.01); G01B 17/08 (2006.01)
CPC B60W 30/02 (2013.01) [B60W 10/06 (2013.01); B60W 10/18 (2013.01); B60W 30/18172 (2013.01); G01B 17/08 (2013.01); B60W 2420/54 (2013.01); B60W 2520/06 (2013.01); B60W 2520/10 (2013.01); B60W 2520/105 (2013.01); B60W 2520/125 (2013.01); B60W 2520/14 (2013.01); B60W 2520/28 (2013.01); B60W 2552/35 (2020.02); B60W 2552/40 (2020.02); B60W 2555/20 (2020.02); B60W 2710/06 (2013.01); B60W 2710/18 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A road surface recognition apparatus comprising:
a memory configured to store one or more instructions; and
a processor configured to execute the one or more instructions stored in the memory,
wherein the instructions, when executed by the processor, cause the processor to:
obtain sound data for a sound detected by a sound detecting sensor;
obtain driving data of a vehicle for driving information detected by a driving information detecting sensor; and
recognize a type of road surface according to the sound data and the driving data, and
wherein the processor is further configured to:
transform the obtained sound data into sound data in a frequency domain;
obtain a first feature vector using longitudinal acceleration data and driving speed data among the driving data, and the sound data in the frequency domain;
classify a first type of the road surface in a first order using the first feature vector and a first classifier model;
obtain a second feature vector according to a plurality of wheel speed data, the longitudinal acceleration data, steering angle data, lateral acceleration data, and yaw rate data among the driving data;
classify a second type of the road surface in a second order using the second feature vector and a second classifier model; and
determine the type of the road surface by selecting at least one of the first type of the road surface and the second type of the road surface based on the longitudinal acceleration data and reference longitudinal acceleration data.