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%.

Fuzzy Identification Based on Tire/Road Adhesion Feature

For identifying the tire/road friction coefficient accurately in real-time to meet the needs of automobile electronic control system and then improving the active safety performance of automobile, the road recognition method based on fuzzy control algorithm was studied in this paper. Adopt a 7-DOF vehicle dynamic model, wheel slip ratio 2 and longitudinal forces Fx as the input of fuzzy controller with fuzzy rules was proposed. The output is the weight coefficient of p-2 functional expression which is related to cl, c2 and c3 proposed by Burckhardt etc. By a simulation experiment of automobile brake on the condition of driving straight or veering on a single road and docking pavement, to some extent, indicates that this method is able to guarantee the real-time and accuracy of the road identification.

Road Identification for Anti-Lock Brake Systems Equipped with Only Wheel Speed Sensors

Anti lock brake systems (ABS) are now widely used on motor vehicles. To reduce product cost and to use currently available technologies, standard ABS uses only wheel speed sensors to detect wheel angular velocities, which is not enough to directly obtain wheel slip ratios needed by the control unit, but can be used to calculate reference slip ratios with measured wheel angular velocities and the estimated vehicle speed. Therefore, the road friction coefficient, which determines the vehicle deceleration during severe braking, is an important parameter in estimating vehicle speed. This paper analyzes wheel acceleration responses in simulations of severe braking on different road surfaces and selects a pair of specific points to identify the wheel acceleration curve for each operating condition, such as road surface, pedal braking torque and wheel vertical load. It was found that the curve using the selected points for each road surface clearly differs from that of the other road surfaces. Therefore, different road surfaces can be distinguished with these selected points which represent their corresponding road surfaces. The analysis assumes that only wheel speed sensors are available as hardware and that the road cohesion condition can be determined in the initial part of the severe braking process.

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.

New Adaptive Approach for Road Condition Identification in ASR Control System

A novel tire-road adaptive model in longitude direction to formulate the dynamic characteristic between tire and road is proposed in this paper, based on this model, a new adaptive approach of road condition identification is presented to identify the model＇s parameters on-line in order to improve the performance of anti-slip regulation system（ASR）. The optimal slip is determined by using the drive wheel＇s slip and longitude traction force in ASR before the slipping of the drive wheel. Co-simulation is done based on the model for JETTA GTX building with ADAMS/CAR and Matlab, and results show that the adaptive model accords with Pacejka model very well. This adaptive model has simpler form, less number of parameters and higher adaptability than usual, and the new identification approach has a small amounts of operation, which is very suitful for ASR.

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.

A novel algorithm to identifying vehicle travel path in elevated road area based on GPS trajectory data

In recent years, the increasing development of traffic information collection technology based on floating car data has been recognized, which gives rise to the establishment of real-time traffic information dissemina- tion system in many cities. However, the recent massive construction of urban elevated roads hinders the processing of corresponding GPS data and further extraction of traffic information （e.g., identifying the real travel path）, as a result of the frequent transfer of vehicles between ground and elevated road travel. Consequently, an algorithm for identifying the travel road type （i.e., elevated or ground road） of vehicles is designed based on the vehicle traveling features, geometric and topological characteristics of the elevated road network, and a trajectory model proposed in the present study. To be specific, the proposed algorithm can detect the places where a vehicle enters, leaves or crosses under elevated roads. An experiment of 10 sample taxis in Shanghai, China was conducted, and the comparison of our results and results that are obtained from visual interpretation validates the proposed algo- rithm.