The subject disclosure relates to techniques for optimizing autonomous vehicle activities at a fleet level. A process of the disclosed technology can include measuring a first set of cost metrics for a first plurality of autonomous vehicles (AVs) associated with a first vehicle routing version, wherein the first set of cost metrics comprises energy consumption values for two or more of the first plurality of AVs, measuring a second set of cost metrics for a second plurality of AVs associated with a second vehicle routing version, wherein the second set of cost metrics comprises energy consumption values for two or more of the second plurality of AVs, and comparing the first set of cost metrics with the second set of cost metrics to determine routing updates configured to reduce fleetwide energy consumption.

A transportation vehicle, a traffic control entity, a method, a computer program, and an apparatus for adapting a speed of transportation vehicles in a platoon. The method for adapting a speed of transportation vehicles in a platoon includes obtaining information related to a future course of required minimum inter-vehicular distances of the transportation vehicles of the platoon. The method also includes adapting a speed of the transportation vehicles of the platoon based on the information related to the future course of the required minimum inter-vehicular distances and a fuel consumption of the transportation vehicles of the platoon.

Systems and methods are provided for identifying an imminent low state of charge of a battery in an autonomous vehicle, and automatically powering down the vehicle before a zero state of charge event occurs. In particular, the autonomous vehicle automatically powers down while there is enough energy in the batteries to restart the vehicle and drive the vehicle a short distance to a charging station.

A predicted energy consumption calculation unit calculates, when an on-vehicle apparatus of a rental vehicle is operated during the period of depositing an article in a deposit space of the rental vehicle, a predicted energy consumption that is a predicted value of the amount of energy consumed by the on-vehicle apparatus. A rentable period determination unit determines a rentable period of the deposit space on the basis of the predicted energy consumption calculated by the predicted energy consumption calculation unit.

In one embodiment, a method includes determining an initial cost volume associated with a plurality of potential trajectories of a vehicle in an environment based on a set of movement restrictions of the vehicle, generating a delta cost volume using the initial cost volume and environment data associated with the environment, wherein the delta cost volume is generated by determining adjustments to the initial cost volume that incorporate observed driving behavior, and scoring a trajectory of the plurality of potential trajectories for the vehicle based on the initial cost volume and the delta cost volume.

Provided are methods for controlling vehicle performance based on data associated with an environmental condition.

A method for fuel efficiency optimization by predictive driving, the method comprises: determining a current state of a vehicle and current state of an environment of the vehicle; estimating a future state of the vehicle and a future state of the environment of the vehicle; wherein a future state of each one of the vehicle and the environment is a state at a future point of time following a current point of time; evaluating, whether the vehicle has to change one or more vehicle progress parameters between the current point of time and the future point of time; selecting a future driving behavior out of multiple future driving behaviors, that will implement the change of the one or more vehicle progress parameters, wherein the selecting is based on a fuel consumption associated with the change of the one or more future driving parameters; and generating at least one of a selected future driving behavior suggestion, a selected future driving behavior alert, and a selected future driving behavior command.

A method includes receiving, iteratively over time, sets of data including vehicle dynamics data, image data, sound data, third-party data, and wind speed sensor data, each detected at an autonomous vehicle and associated with a time period. The method also includes estimating a first wind speed and a first wind direction for each time period, in response to receiving the sets of data and based on the sets of data, via a processor of the autonomous vehicle. The method also includes iteratively modifying a lateral control and/or a longitudinal control of the autonomous vehicle based on the estimated first wind speed and the estimated first wind direction, via the processor of the autonomous vehicle and during operation of the autonomous vehicle.

An autonomous vehicle may determine that it has an amount of power remaining projected to be needed to reach a recharging point by autonomously traveling with predefined systems disabled. The vehicle may disable the predefined systems and travel towards the recharging point. Subsequent to the disabling, the autonomous vehicle may determine that it no longer has the amount of power remaining projected to be needed to reach the recharging point. The vehicle may then communicate with a server and request assistance. The vehicle may then travel to an instructed rendezvous point with a second autonomous vehicle, the rendezvous received from the server. The two vehicles may then communicate to allow the first vehicle to leverage a capability of the second autonomous vehicle, responsive to the first and second autonomous vehicles being within communication range, allowing the first vehicle to reach the recharging point via the leveraging.

Sensor data captured by one or more sensors may be received at an analysis system. A neural network may be used to detect an object in the sensor data. A plurality of polygons surrounding the object may be generated in one or more subsets of the sensor data. A prediction of a future position of the object may be generated based at least in part on the polygons. One or more commands may be provided to a control system based on the prediction of the future position.