New Method of Road Surface Identification in Anti-Lock Braking System

Based on the vehicle-road dynamic model, the road characteristic parameter, which depends on different types of road surfaces, is introduced and a new method of road surface identification for automotive anti-lock braking system (ABS) is proposed. According to the characteristics of vehicle-road dynamic model, a simple math resolution method of the model's factors is established. Only using the information of wheel speed, the vehicle reference velocity and the wheel slip ratio are estimated real-timely. And based on the wheel dynamic model, the road characteristic parameter is determined to identify the road surface for the determination of thresholds of ABS regulative parameters. With this new method, the road surface identification can be accurately obtained and calculation time is short that it can meet the ABS real time control need, and it also improves the performance of ABS.

Road identification method based on angular acceleration of vehicle driving wheel

The fundamental principle of road identification by using angular acceleration of driving wheels was demonstrated in this paper. Based on the analysis of energy conversion and parameters variation during the vehicle drive slip process, the change of adhesion coefficient relative to the an- gular acceleration were theoretically studied experimentally validated. The variation shows that the change of adhesion coefficient relative to the angular acceleration and the change of slip ratio in the drive slip process have same trend-both of them exist an only optimal angular acceleration corre- sponding to the peak value of adhesion coefficient. The peak adhesion coefficient of the prototype vehicle is about 0. 14 on the ice-covered road surfaces, with the corresponding optimal angular accel- eration of about 23.5 rad/s2 and optimal slip ratio of about 9. 4%.

Robust Identification of Road Surface Condition Based on Ego‑Vehicle Trajectory Reckoning

The type of road surface condition(RSC)will directly affect the driving performance of vehicles.Monitoring the type of RSC is essential for both transportation agencies and individual drivers.However,most existing methods are solely based on a dynamics-based method or an image-based method,which is susceptible to road excitation limitations and interference from the external environment.Therefore,this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will expe-rience.First,a road feature extraction model based on multi-task learning is conducted,which can simultaneously segment the drivable area and road cast shadow.Second,the optimized candidate regions of interest are classified with confidence levels by ShuffleNet.Considering environmental interference,candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results.Finally,the ego-vehicle trajectory reckoning module based on the kinematic bicycle model is added to the proposed fusion method to extract the RSC experienced by the front wheels.The performance of the entire framework is verified on a specific dataset with shadow and split curve roads.The results reveal that the proposed method can identify the RSC with accurate predictions in real time.