Abstract: Predictive maintenance and diagnostics for an electronic module of an autonomous vehicle using modular condition monitoring is described herein. A computing system receives a signal from a data logger which monitors a condition of the electronic module of the autonomous vehicle, wherein the signal is indicative of damage accumulation information thereof. The computing system identifies a type of the electronic module and a damage accumulation threshold for the type of the electronic module to generate a predicted maintenance schedule for the electronic module of the autonomous vehicle. The damage accumulation information can be stored in a data store to define the damage accumulation threshold for the type of the electronic module.

Abstract: Described herein is a server computing system that controls an autonomous vehicle to perform an operation to at least one of measure or isolate an effect of a variable on an actual power consumption by the autonomous vehicle. Data indicative of the actual power consumption, which is generated based on the operation, and data indicative of a projected power consumption, which is accumulated based on prior execution of the operation by a same or different autonomous vehicle, is received by the server computing system to determine whether an energy efficiency of the autonomous vehicle is degraded. The operation may be performed to identify a degraded vehicle system or component of the autonomous vehicle or to identify an autonomous vehicle in a fleet of autonomous vehicles for which further analysis is desirable. An output is generated by the server computing system that is indicative of the energy efficiency prognostics.

Abstract: Described herein is a server computing system that controls an autonomous vehicle to perform an operation to at least one of measure or isolate an effect of a variable on an actual power consumption by the autonomous vehicle. Data indicative of the actual power consumption, which is generated based on the operation, and data indicative of a projected power consumption, which is accumulated based on prior execution of the operation by a same or different autonomous vehicle, is received by the server computing system to determine whether an energy efficiency of the autonomous vehicle is degraded. The operation may be performed to identify a degraded vehicle system or component of the autonomous vehicle or to identify an autonomous vehicle in a fleet of autonomous vehicles for which further analysis is desirable. An output is generated by the server computing system that is indicative of the energy efficiency prognostics.

Abstract: Described herein is a system and method for detecting malodor within a cabin of an autonomous vehicle, wherein initiation of a remediation system and/or dispatch of the autonomous vehicle to a service hub for mitigating the malodor is based upon sensor signals that monitor the cabin of the autonomous vehicle. The remediation system can include devices such as an HVAC system or a window, which are operated to reduce the concentration of airborne molecules in the cabin of the autonomous vehicle when the concentration of airborne molecules exceeds an air quality threshold. A dispatch protocol may be initiated to dispatch the autonomous vehicle to a service hub when the malodor is associated with certain predefined incidents or when remediation fails to reduce the concentration of airborne molecules below the air quality threshold.

Abstract: Described herein is a server computing system that controls an autonomous vehicle to perform an operation to at least one of measure or isolate an effect of a variable on an actual power consumption by the autonomous vehicle. Data indicative of the actual power consumption, which is generated based on the operation, and data indicative of a projected power consumption, which is accumulated based on prior execution of the operation by a same or different autonomous vehicle, is received by the server computing system to determine whether an energy efficiency of the autonomous vehicle is degraded. The operation may be performed to identify a degraded vehicle system or component of the autonomous vehicle or to identify an autonomous vehicle in a fleet of autonomous vehicles for which further analysis is desirable. An output is generated by the server computing system that is indicative of the energy efficiency prognostics.

Abstract: Described herein is a server computing system that controls an autonomous vehicle to perform an operation to at least one of measure or isolate an effect of a variable on an actual power consumption by the autonomous vehicle. Data indicative of the actual power consumption, which is generated based on the operation, and data indicative of a projected power consumption, which is accumulated based on prior execution of the operation by a same or different autonomous vehicle, is received by the server computing system to determine whether an energy efficiency of the autonomous vehicle is degraded. The operation may be performed to identify a degraded vehicle system or component of the autonomous vehicle or to identify an autonomous vehicle in a fleet of autonomous vehicles for which further analysis is desirable. An output is generated by the server computing system that is indicative of the energy efficiency prognostics.

Abstract: Described herein is a system and method for detecting malodor within a cabin of an autonomous vehicle, wherein initiation of a remediation system and/or dispatch of the autonomous vehicle to a service hub for mitigating the malodor is based upon sensor signals that monitor the cabin of the autonomous vehicle. The remediation system can include devices such as an HVAC system or a window, which are operated to reduce the concentration of airborne molecules in the cabin of the autonomous vehicle when the concentration of airborne molecules exceeds an air quality threshold. A dispatch protocol may be initiated to dispatch the autonomous vehicle to a service hub when the malodor is associated with certain predefined incidents or when remediation fails to reduce the concentration of airborne molecules below the air quality threshold.

Abstract: Predictive maintenance and diagnostics for an electronic module of an autonomous vehicle using modular condition monitoring is described herein. A computing system receives a signal from a data logger which monitors a condition of the electronic module of the autonomous vehicle, wherein the signal is indicative of damage accumulation information thereof. The computing system identifies a type of the electronic module and a damage accumulation threshold for the type of the electronic module to generate a predicted maintenance schedule for the electronic module of the autonomous vehicle. The damage accumulation information can be stored in a data store to define the damage accumulation threshold for the type of the electronic module.

Abstract: Predictive maintenance and diagnostics for an electronic module of an autonomous vehicle using modular condition monitoring is described herein. A computing system receives a signal from a data logger which monitors a condition of the electronic module of the autonomous vehicle, wherein the signal is indicative of damage accumulation information thereof. The computing system identifies a type of the electronic module and a damage accumulation threshold for the type of the electronic module to generate a predicted maintenance schedule for the electronic module of the autonomous vehicle. The damage accumulation information can be stored in a data store to define the damage accumulation threshold for the type of the electronic module.

Abstract: Described herein is a server computing system that controls an autonomous vehicle to perform an operation to at least one of measure or isolate an effect of a variable on an actual power consumption by the autonomous vehicle. Data indicative of the actual power consumption, which is generated based on the operation, and data indicative of a projected power consumption, which is accumulated based on prior execution of the operation by a same or different autonomous vehicle, is received by the server computing system to determine whether an energy efficiency of the autonomous vehicle is degraded. The operation may be performed to identify a degraded vehicle system or component of the autonomous vehicle or to identify an autonomous vehicle in a fleet of autonomous vehicles for which further analysis is desirable. An output is generated by the server computing system that is indicative of the energy efficiency prognostics.

Abstract: Described herein is a system and method for detecting malodor within a cabin of an autonomous vehicle, wherein initiation of a remediation system and/or dispatch of the autonomous vehicle to a service hub for mitigating the malodor is based upon sensor signals that monitor the cabin of the autonomous vehicle. The remediation system can include devices such as an HVAC system or a window, which are operated to reduce the concentration of airborne molecules in the cabin of the autonomous vehicle when the concentration of airborne molecules exceeds an air quality threshold. A dispatch protocol may be initiated to dispatch the autonomous vehicle to a service hub when the malodor is associated with certain predefined incidents or when remediation fails to reduce the concentration of airborne molecules below the air quality threshold.

Abstract: Predictive maintenance and diagnostics for an electronic module of an autonomous vehicle using modular condition monitoring is described herein. A computing system receives a signal from a data logger which monitors a condition of the electronic module of the autonomous vehicle, wherein the signal is indicative of damage accumulation information thereof. The computing system identifies a type of the electronic module and a damage accumulation threshold for the type of the electronic module to generate a predicted maintenance schedule for the electronic module of the autonomous vehicle. The damage accumulation information can be stored in a data store to define the damage accumulation threshold for the type of the electronic module.