Vehicle control system
A system includes one or more processors that may determine one or more of an energy drag or a parasitic energy loss for upcoming planned travel of a vehicle along one or more routes based on externality information. The one or more processors may determine the one or more of the energy drag or the parasitic energy loss for each of plural, different route locations along the one or more routes and may change one or more aspects of the upcoming planned travel of the vehicle based on the one or more of energy drag or parasitic energy loss that is determined.
1. A system comprising:
one or more processors configured to determine one or more of an energy drag or a parasitic energy loss for upcoming planned travel of a vehicle along one or more routes based on externality information,
the one or more processors are configured to determine the one or more of the energy drag or the parasitic energy loss for each of plural, different route locations along the one or more routes and to change movement of the vehicle based on the one or more of energy drag or parasitic energy loss that is determined.
2. The system of claim 1 , wherein the one or more processors are configured to use an artificial intelligence or machine learning model of the one or more of the energy drag or the parasitic energy loss to determine the one or more of the energy drag or the parasitic energy loss for at least one of the plural, different route locations along the one or more routes.
3. The system of claim 1 , further comprising: one or more onboard sensors configured to measure vehicle locations associated with the externality information.
4. The system of claim 1 , wherein the one or more processors are configured to determine one or more designated speeds at which the vehicle is to move based on the one or more of energy drag or parasitic energy loss that is determined.
5. The system of claim 1 , wherein the one or more processors are configured to change at least one route of the one or more routes on which the vehicle is scheduled to travel along based on the one or more of the energy drag or the parasitic energy loss that is determined.
6. The system of claim 1 , wherein the one or more processors are configured to change a time at which the vehicle is to travel based on the one or more of the energy drag or the parasitic energy loss that is determined.
7. The system of claim 1 , wherein the one or more processors are configured to determine one or more of a wind speed or a wind direction as the one or more of the energy drag or the parasitic energy loss.
8. A method comprising:
determining one or more of an energy drag or a parasitic energy loss for upcoming planned travel of a vehicle along one or more routes based on externality information;
determining the one or more of the energy drag or the parasitic energy loss for each of plural, different route locations along the one or more routes; and
changing movement of the vehicle based on the one or more of energy drag or parasitic energy loss that is determined.
9. The method of claim 8 , wherein determining the one or more of the energy drag or the parasitic energy loss for at least one of the plural, different route locations along the one or more routes is performed using an artificial intelligence or machine learning model of the one or more of the energy drag or the parasitic energy loss.
10. The method of claim 8 , further comprising: measuring vehicle locations associated with the externality information.
11. The method of claim 8 , comprising determining one or more designated speeds at which the vehicle is to move as based on the one or more of energy drag or parasitic energy loss that is determined.
12. The method of claim 8 , comprising changing at least one route of the one or more routes on which the vehicle is scheduled to travel along based on the one or more of the energy drag or the parasitic energy loss.
13. The method of claim 8 , comprising changing a time at which the vehicle is to travel based on the one or more of the energy drag or the parasitic energy loss that is determined.
14. The method of claim 8 , wherein the one or more of the energy drag or the parasitic energy loss includes one or more of a wind speed or a wind direction.
15. A system comprising:
one or more processors configured to determine wind speed and wind direction for plural locations along one or more routes using an artificial intelligence or machine learning model, the one or more processors also configured to determine one or more of wind drag or a parasitic energy loss for travel by a vehicle along the one or more routes based on the wind speed and wind direction,
wherein the one or more processors are configured to change movement of the vehicle for travel along the one or more routes based on the one or more of wind drag or parasitic energy loss that is determined using the artificial intelligence or machine learning model.
16. The system of claim 15 , wherein the one or more processors are configured to change one or more of a planned speed at which the vehicle is planned to travel along the one or more routes, a planned route on which the vehicle is planned to travel along, or a planned time at which the vehicle is to travel.
17. The system of claim 15 , wherein the one or more processors are configured to determine a coefficient of drag as a function of apparent wind yaw angle and to determine the one or more of wind drag or parasitic energy loss based on the wind speed, the wind direction, and the coefficient of drag that is determined.
18. The system of claim 15 , wherein the one or more processors are configured to determine which of different groups of additional vehicles that the vehicle is to travel with based on the one or more of wind drag or parasitic energy loss that is determined.