Provided is a vehicle control device capable of accurately estimating a self-vehicle location with a yaw angle error occurring when a vehicle starts to travel due to an initial phase shift between pulsed waveforms of left and right wheels when the vehicle starts to travel suppressed. The vehicle control device further estimates a direction of wheel rotation while the vehicle is at a stop. A yaw angle deviation when the vehicle starts to travel is estimated and corrected on the basis of a weighted average of discrete yaw angle values obtained immediately after the vehicle starts to travel from wheel speed sensors installed on left and right non-steered wheels of the vehicle. A yaw angle while the vehicle is at a stop and a yaw angle when the vehicle starts to travel are also estimated, and the direction of wheel rotation while the vehicle is at a stop is also estimated from a difference between the yaw angles. The yaw angle and coordinates are corrected on the basis of the estimation result.

The present teaching relates to method, system, and medium, for operating a vehicle. Real-time data related to the vehicle are received. A current mode of operation of the vehicle and a state of the driver present in the vehicle are determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data and the state of the driver in accordance with a risk model. In response to the first risk satisfying a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined based on the state of the driver. The vehicle is switched from the current mode to the different mode when the second risk satisfies a second criterion.

In an apparatus for estimating a behavior of a vehicle carrying the apparatus based on images of surroundings of the vehicle captured by an imaging device, an information acquirer acquires beforehand specific location information that is information representing a specific location in which a situation around the vehicle is such that the estimation of the own vehicle behavior based on the images is unstable. In the apparatus, a behavior estimator estimates the own vehicle behavior based on the images captured by the imaging device and the specific location information.

In an apparatus for estimating a behavior of a vehicle carrying the apparatus based on images of surroundings of the vehicle captured by an imaging device, an information acquirer acquires beforehand specific location information that is information representing a specific location in which a situation around the vehicle is such that the estimation of the own vehicle behavior based on the images is unstable. In the apparatus, a behavior estimator estimates the own vehicle behavior based on the images captured by the imaging device and the specific location information.

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 set of first candidate topologies of first candidate roadside infrastructure nodes at respective mounting locations in a geographic area is randomly generated. For each of the first candidate topologies, first simulations, including detection of objects according to selected sensor parameters, installation parameters, and environment parameters for the candidate nodes at the respective mounting locations, are executed. First fitness scores are determined for each of the first candidate topologies by comparing results of the first simulations to ground truth data. Upon identifying one of the first fitness scores as exceeding a threshold, the candidate topology associated with the identified first fitness score is identified for deployment.

A set of first candidate topologies of first candidate roadside infrastructure nodes at respective mounting locations in a geographic area is randomly generated. For each of the first candidate topologies, first simulations, including detection of objects according to selected sensor parameters, installation parameters, and environment parameters for the candidate nodes at the respective mounting locations, are executed. First fitness scores are determined for each of the first candidate topologies by comparing results of the first simulations to ground truth data. Upon identifying one of the first fitness scores as exceeding a threshold, the candidate topology associated with the identified first fitness score is identified for deployment.

Dynamic braking capability of a combination vehicle including a tractor and at least one trailer is provided based on a distribution of the load carried by the combination vehicle. Load distribution is determined directly using load sensors disposed at wheel pairs of the tractor and trailer(s) or indirectly by using a load sensor located at the drive axle of the tractor together with engine torque and vehicle speed signals for determining gross vehicle mass. A database having sub-databases therein each storing stopping distance calculation results for a corresponding combination vehicle type e.g. 5-axle single or 8-axle double, is indexed by using the determined load distributions for providing the dynamic braking capability based on the vehicle type and its load distribution. The database may also be indexed using Axle Load Allocation Factor that is calculated based on a mathematical combination of drive, steering, and gross trailer axle loading.

Dynamic braking capability of a combination vehicle including a tractor and at least one trailer is provided based on a distribution of the load carried by the combination vehicle. Load distribution is determined directly using load sensors disposed at wheel pairs of the tractor and trailer(s) or indirectly by using a load sensor located at the drive axle of the tractor together with engine torque and vehicle speed signals for determining gross vehicle mass. A database having sub-databases therein each storing stopping distance calculation results for a corresponding combination vehicle type e.g. 5-axle single or 8-axle double, is indexed by using the determined load distributions for providing the dynamic braking capability based on the vehicle type and its load distribution. The database may also be indexed using Axle Load Allocation Factor that is calculated based on a mathematical combination of drive, steering, and gross trailer axle loading.