A control method of mixed traffic flow on freeway ramp based on controllable connected and autonomous vehicles (CAVs) is provided. A ramp is divided into a normal driving section, a vehicle platoon formation section and an accelerating and merging section. A vehicle platoon is formed by a leading CAV and human-driven vehicles (HDVs). Time interval [t.sub.min, t.sub.max] for the vehicle platoon to completely reach the merging point S is calculated. CAVs on the main lane and ramp are cooperatively controlled, and a merging gap is reserved for the ramp vehicle platoon. The vehicle platoon is allowed to accelerate and merge into the main lane. By means of the Internet-of-Vehicle (IoV) technology, the traffic situation on the main lane and downstream merging zone can be obtained in advance, and speeds of the CAVs are cooperatively controlled to lead the ramp vehicles to safely merge into the main lane.

This disclosure is generally directed to systems and methods for detecting a water depth level using capacitive sensors. The systems and methods disclosed herein receive a first capacitive signal from a first capacitive sensor in a wheel well of an autonomous vehicle (AV) and determine that the first capacitive signal exceeds a threshold value. The AV controller may be configured to determine water levels using a capacitive sensor system, and perform mitigating actions that cause the vehicle to either clean soiled capacitive sensors, or move the vehicle to a location that mitigates the risk of vehicle damage. Other mitigating actions may be performed as well, including disabling or powering down critical vehicle components when the vehicle cannot be moved to another location, providing means for emergency vehicle exit, and sending warning messages to the fleet control server, to occupants of the AV, or to other emergency personnel.

A method 20 and system 10 monitor road condition, by providing, for portions (14.1-14.m) of a road 12, an approximation 210 of a roughness figure in accordance with a roughness index. The method 10 includes receiving speed data 208 of a first vehicle 16 travelling along each of the portions of the road 12 and receiving, from a measuring device 18 carried on the first vehicle 16, measured acceleration data 204 of the device 18 perpendicular to the road 12 surface. The acceleration data 204 is processed to provide a parameter value 206 relating to the acceleration data 204 for each of the portions of the road 12. A first speed-based conversion equation and the speed data 208 is utilized to convert the parameter 206 into the approximation 210 of a roughness figure for each of the portions of the road 12, in accordance with the roughness index.

Described are devices, systems and methods for managing power generation, storage and/or distribution in autonomous vehicles. In some aspects, a system for power management in an autonomous vehicle having a main power source and one or more alternators includes a vehicle control unit, a secondary power source, and a power management unit. In some embodiments, the power management unit is configured on an autonomous vehicle having a single alternator-charging system for battery charging and battery power control with different battery packs. In some embodiments, the power management unit is configured on an autonomous vehicle having multiple alternator-charging systems for battery charging and battery power control for different battery packs.

A method for operating a driver assistance system of a vehicle involves, for determining a position of the vehicle in a digital environment map, detecting environment data for the vehicle using an on-board sensor system and matched with map data stored in the environment map. A position of the vehicle in a real environment is determined using position data of the vehicle from an on-board satellite receiver. Accuracy of the determined position of the vehicle is determined based on the position data and environment data matched with the environment data. Accuracy is predicted with which the position of the vehicle in the environment map can be determined for a predetermined section of road ahead of the vehicle. Fully automated operation of the vehicle is enabled when the determined accuracy and the predicted accuracy for the predetermined section of road ahead of the vehicle are greater than at least one accuracy threshold.

A system for controlling a compressor may include an engine controller that controls a fuel injection amount corresponding to an engine load and an opening amount of a throttle by reflecting a required torque required for an air conditioner (A/C), an operation information detector for detecting operation information according to driving state of the vehicle, a compressor that generates pressure during operation of the A/C, an air conditioner relay which is turned on when the air conditioner operates and is turned off when the A/C is stopped, and a controller which determines an engine negative pressure of an intake manifold, and when the cooling water temperature is lower than the predetermined temperature and the intake manifold pressure is lower than the first threshold value, a cold-start intake manifold negative pressure insufficient event is generated to reduce the A/C duty in accordance with the entry into a negative pressure recovery mode.

A vehicle control device includes: an automatic driving control unit that executes a first driving mode in which at least one of acceleration/deceleration, and steering of a host vehicle is automatically controlled in order for the host vehicle to travel along a route up to a destination; and a specific situation transition control unit that encourages a vehicle occupant of the host vehicle to transition to a second driving mode in which the degree of automatic driving is lower in comparison to the first driving mode by decelerating the host vehicle in a case of terminating execution of the first driving mode at a scheduled termination point of the first driving mode.

Methods, devices and apparatuses pertaining to U-turn assistance. The method may include detecting an intention of an operator of a vehicle of rendering a U-turn at a location. A computing device may obtain geographic information associated with the U-turn, and assess feasibility of the U-turn at the location based on the geographic information. Further, the computing device may provide a notification to the operator based on the feasibility of the U-turn to assist the operator to operate the U-turn of the vehicle at the location.

A method and an apparatus for controlling an autonomous driving vehicle are provided. The method includes: receiving environment information sent by an autonomous driving vehicle, the environment information including vehicle exterior environment information; determining whether the autonomous driving vehicle is in an abnormal operation status, based on the vehicle exterior environment information and operation information of an operation executed by the autonomous driving vehicle; and sending a braking control instruction and a data acquisition instruction to the autonomous driving vehicle, in response to determining that the autonomous driving vehicle is in the abnormal operation status, the braking control instruction being used for controlling braking of the autonomous driving vehicle, and the data acquisition instruction being used for acquiring data of a driving recorder in the autonomous driving vehicle.

An axle load measuring apparatus includes a displacement calculator, a storage, and an axle load calculator. The displacement calculator detects displacements of positions on a road caused by an axle load using a captured image of the road and a vehicle thereon. When a certain amount of load is applied to a predetermined position of the road, the storage stores a displacement function representing shape information of a spatial distribution of a displacement of the road originated from the predetermined position. The axle load calculator calculates the axle load based on the displacements of the positions and the displacement function.