In some implementations, a route selection system obtains a first scheduled time for a first event at a venue for a passenger of an autonomous vehicle. The system determines whether the autonomous vehicle will arrive at the venue by the first scheduled time. The system obtains, in response to a determination that the autonomous vehicle will not arrive at the venue by the first scheduled time, a second scheduled time for a second event at the venue. The system determines a route to the venue based on one or more preferences of the passenger, one or more preferences of an operator of the autonomous vehicle, or any combination thereof, wherein the determined route is configured for arrival of the passenger at the venue after the first scheduled time and at or within a time period before the second scheduled time.

An image captured by a far-infrared camera is analyzed to analyze a distribution of a road-surface temperature, and a course of a highest road-surface temperature is determined to be a traveling route. Further, automatic driving along the course of the highest road-surface temperature is performed. Furthermore, a state of the distribution of a road-surface temperature, and a direction of the course of the highest road-surface temperature are displayed on a display section, so that a user (a driver) recognizes them. For example, a state analyzer detects a candidate course travelable for a vehicle, the state analyzer detecting a plurality of the candidate courses, calculates an average value of a road-surface temperature of each of the plurality of the candidate courses, and determines the candidate course having a largest average value of a road-surface temperature to be a traveling route.

A vehicle control device or method controls an output of a drive source of a vehicle based on a color of an illuminated signal of a traffic light in a vehicle-advancing direction during travel in autonomous driving. The vehicle control device includes a control unit that estimates the color of the currently illuminated signal of the traffic light based on oncoming vehicle information and controls the output of the drive source based on an estimation result when the color of the illuminated signal of the traffic light cannot be acquired by the onboard camera. The control unit limits the output of the drive source and reduces a vehicle speed from a current vehicle speed to a vehicle speed at which fuel efficiency is superior to that at the current vehicle speed when the color of the illuminated signal of the traffic light is estimated to be red.

A calibration table usable in operating an autonomous driving vehicle (ADV) is updated. The operations comprise: determining a first torque value at a first time instant prior to executing a control command; determining a control command based on a speed of the ADV, a desired acceleration, and an associated entry in the calibration table; executing the control command; determining a second torque value at a second time instant subsequent to executing the control command; determining a torque error value as a difference between the first and second torque values; updating the associated entry in the calibration table based at least in part on the torque error value; and generating driving signals based at least in part on the updated calibration table to control operations of the ADV.

A system for providing a speed profile of a self-driving vehicle includes a vehicle driving information prediction device, and a speed profile generation device, wherein the vehicle driving prediction device includes a navigation unit configured to set information on a drive route and a target travel time, a 3D map information provision unit configured to search for gradient information of the drive route set by the navigation unit, and a vehicle driving information provision unit, and wherein the speed profile generation device includes a vehicle energy consumption calculation unit configured to calculate energy consumption at a current speed of the vehicle when the vehicle runs along the set drive route, and a speed profile calculation unit configured to calculate a distance-based target speed profile by executing a dynamic programming algorithm.

The disclosure generally pertains to minimizing battery power consumption while employing local wireless communication to activate an operation of an autonomous vehicle. In an example implementation, a vehicle controller of an autonomous vehicle transitions to a powered-down state after the autonomous vehicle is parked at a parking spot that lacks cellular communication coverage. The vehicle controller may transition to a powered-up state at a scheduled time to execute an autonomous operation based on a directive stored in a cloud-based device. In no directive has been stored, the vehicle controller wakes up periodically in a partially powered-up state and transmits a query in a local wireless communications format to the cloud-based device to check for a directive. If no directive is present, the vehicle controller transitions back to the powered-down state. If a directive is present, the vehicle controller transitions to a fully powered-up state to execute the autonomous operation.

An energy management system determines two or more fuel components that represent fuel consumption by a vehicle system completing a trip over one or more routes. A trip plan that designates operational settings of the vehicle system at one or more of different locations, different distances along the one or more routes, or different times is generated or modified. The trip plan is based on the fuel components. The fuel components include a delta elevation component of the one or more routes, a delta speed component of the trip, a mean drag component of the vehicle system, a curvature component of the one or more routes, a base fuel component of the vehicle system, a minimum braking component of the vehicle system, a braking auxiliaries component of the vehicle system, and/or a drag variation of the vehicle system.

A controller obtains topographical data indicative of the topography of a work site. The controller obtains material data indicative of the position of a material. The controller computes an evaluation function based on the material data for each of a plurality of candidates of the travel path to be decided from the topographical data. The evaluation function includes a material function pertaining to an amount of the material. The controller decides a candidate having a smallest evaluation function of the plurality of candidates as the travel path.

There is no consideration on performing charging control on a battery necessary for limp-home travel according to a travel environment of a vehicle. It is assumed that the vehicle changes a travel lane to a second travel lane by overtaking or the like between time t1 and t0. The second power generation threshold generation unit 33 refers to the lookup table 50 illustrated in FIG. 5 and reads 70 as the second charging threshold SOCth2. Then, the second charging threshold SOCth2 is larger than the first charging threshold SOCth1 as illustrated in FIG. 6(C), and thus, the threshold selection unit 34 outputs the second charging threshold SOCth2 as the selected charging threshold SOCth. At this time, the SOC of the battery is lower than the charging threshold SOCth, and thus, the power generation command value GEN is turned on at time t1, and the power generation engine is started to charge the battery. As a result, when the vehicle is traveling on the second travel lane which is far from an evacuation road 407, a large amount of energy is required for a limp-home operation for returning to the evacuation road 407, and thus, the battery can be sufficiently charged.

Methods and systems for enhancing the functionality of a semi-autonomous vehicle are described herein. The semi-autonomous vehicle may receive a communication from a fully autonomous vehicle within a threshold distance of the semi-autonomous vehicle. If the vehicles are travelling on the same route or the same portion of a route, the semi-autonomous vehicle may navigate to a location behind the fully autonomous vehicle. Then the semi-autonomous vehicle may operate autonomously by replicating one or more functions performed by the fully autonomous vehicle. The functions and/or maneuvers performed by the fully autonomous vehicle may be detected via sensors in the semi-autonomous vehicle and/or may be identified by communicating with the fully autonomous vehicle to receive indications of upcoming maneuvers. In this manner, the semi-autonomous vehicle may act as a fully autonomous vehicle.