Systems and methods are provided for generating data indicative of a friction associated with a driving surface, and for using the friction data in association with one or more vehicles. In one example, a computing system can detect a stop associated with a vehicle and initiate a steering action of the vehicle during the stop. The steering action is associated with movement of at least one tire of the vehicle relative to a driving surface. The computing system can obtain operational data associated with the steering action during the stop of the vehicle. The computing system can determine a friction associated with the driving surface based at least in part on the operational data associated with the steering action. The computing system can generate data indicative of the friction associated with the driving surface.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A method for planning a vehicle trajectory includes: acquiring an initial reference trajectory of a target vehicle within a target planning duration, the initial reference trajectory including an initial state variable and an initial control variable of the target vehicle at at least one position point within the target planning duration; acquiring a reference lane trajectory; determining a trajectory cost of the target vehicle according to a geometric constraint and a dynamics constraint by using the reference lane trajectory and the initial state variable and the initial control variable of the target vehicle at the at least one position point, the dynamics constraint including at least one of an energy loss, an acceleration constraint, or an angular speed constraint; and adjusting the initial reference trajectory of the target vehicle to a target travelling trajectory according to the trajectory cost of the target vehicle.

A method for planning a vehicle trajectory includes: acquiring an initial reference trajectory of a target vehicle within a target planning duration, the initial reference trajectory including an initial state variable and an initial control variable of the target vehicle at at least one position point within the target planning duration; acquiring a reference lane trajectory; determining a trajectory cost of the target vehicle according to a geometric constraint and a dynamics constraint by using the reference lane trajectory and the initial state variable and the initial control variable of the target vehicle at the at least one position point, the dynamics constraint including at least one of an energy loss, an acceleration constraint, or an angular speed constraint; and adjusting the initial reference trajectory of the target vehicle to a target travelling trajectory according to the trajectory cost of the target vehicle.

A system for monitoring an acoustic scene outside a vehicle; the system including: a vehicle with wheels and a trunk, an acoustic sensor disposed in the trunk, a control unit operatively connected to the acoustic sensor, and at least one neural network operatively connected to the control unit, and trained in such a way to correlate the characteristics of an audio signal with types of road surfaces; the control unit is configured in such a way to receive an audio signal detected by the acoustic sensor while the vehicle is traveling, extract the characteristics of the audio signal and input said characteristics of the audio signal to the neural network in order to identify the type of road surface covered by the vehicle wheels.

A method for adaptive estimation of a road surface adhesion coefficient for a vehicle with complex excitation conditions taken into consideration comprises the following steps: 1) designing an estimator according to a single-wheel dynamics model of a vehicle, and estimating a longitudinal tire force and a road surface peak adhesion coefficient under longitudinal excitation; 2) designing an estimator according to a two-degree-of-freedom kinematic model of the vehicle, and estimating a tire aligning moment and a road surface peak adhesion coefficient under excitation of a lateral force; and 3) determining an excitation condition met by the vehicle according to a vehicle state parameter, performing fuzzy inference to obtain limits achievable by current longitudinal and lateral tire forces, and designing a fusion observer to fuse estimation results. The method achieves favorable robustness, improves real-time capability, and can be performed quickly and accurately.

A method for adaptive estimation of a road surface adhesion coefficient for a vehicle with complex excitation conditions taken into consideration comprises the following steps: 1) designing an estimator according to a single-wheel dynamics model of a vehicle, and estimating a longitudinal tire force and a road surface peak adhesion coefficient under longitudinal excitation; 2) designing an estimator according to a two-degree-of-freedom kinematic model of the vehicle, and estimating a tire aligning moment and a road surface peak adhesion coefficient under excitation of a lateral force; and 3) determining an excitation condition met by the vehicle according to a vehicle state parameter, performing fuzzy inference to obtain limits achievable by current longitudinal and lateral tire forces, and designing a fusion observer to fuse estimation results. The method achieves favorable robustness, improves real-time capability, and can be performed quickly and accurately.

The disclosure relates to a method for recognizing roughness of a road on which a vehicle is traveling, a vehicle, and a computer-readable storage medium. The method for recognizing roughness of a road on which a vehicle is traveling includes the steps of: A. obtaining a traveling speed of the vehicle on a current road; B. determining whether the obtained traveling speed is not greater than a preset threshold, and if the obtained traveling speed is not greater than the preset threshold, obtaining operating data of the vehicle at a preset time interval; and C. determining the roughness of the current road of the vehicle based on a change feature of the obtained operating data. Through application of the disclosure, roughness of a road on which a vehicle is traveling can be quickly, accurately, and efficiently detected, different road conditions such as uphill, downhill, and various obstacles that can or cannot be crossed over can be recognized, thereby improving the low-speed control capability of the vehicle and enhancing the safety performance of the vehicle.

An estimation apparatus performs non-contact estimation of a friction coefficient of a road surface. The estimation apparatus includes at least one processor. The processor determines a state of the road surface and determine which of preset road-surface states the state of the road surface belongs to. The processor performs primary identification of a first range of a friction coefficient corresponding to the determined road-surface state on the basis of friction coefficient information and the determined state of the road surface. The friction coefficient information is sectioned for each of the road-surface states. The processor narrows down a range of the friction coefficient from the first range to a second range on the basis of the identified first range of the friction coefficient, and thereby perform secondary identification of the friction coefficient of the road surface. The second range is narrower than the first range.