Autonomous Driving Route Constraints for Fuel-Efficient Grade Handling
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Solution Overview
Problem
Conventional autonomous vehicle routing systems lack the ability to consider long-term driving strategies based on physical vehicle and route data, leading to inefficient fuel consumption, premature wear of components, and potential collisions due to inappropriate vehicle actions.
Innovation Solution
Implementing physics-informed optimization by generating driving constraint data that accounts for physical vehicle and route characteristics, such as grade and length, to guide short time horizon routing decisions, ensuring appropriate vehicle actions are taken.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional autonomous vehicle routing systems use short time horizon routing decisions, then computational overhead is reduced, but fuel efficiency deteriorates and wear-and-tear on vehicle components increases
Solution Approach 1:
The system performs preliminary identification of grade segments with threshold grade values and pre-generates driving constraint data based on grade data and physical vehicle data before routing decisions are made. This advance preparation allows the routing module to use pre-computed constraints without adding computational overhead during real-time routing, thereby improving fuel efficiency through long-term strategic planning while maintaining low computational complexity
Solution Approach 2:
The route is divided into segments based on grade data, with specific segments identified that meet threshold grade values. By segmenting the route and applying driving constraints specifically to grade segments rather than the entire route, the system optimizes fuel efficiency where needed without unnecessarily complicating the overall routing computation
2Reliability
If conventional autonomous vehicle routing systems use short time horizon routing decisions, then response time is improved, but reliability deteriorates due to potential collisions from inappropriate vehicle actions
Solution Approach 1:
Driving constraint data is generated in advance based on grade data and physical vehicle data before routing decisions are executed. This preliminary constraint generation ensures that safety-critical information about grade segments is prepared beforehand, allowing the routing module to make reliable collision-free decisions without delaying the routing response
Solution Approach 2:
Driving constraint data acts as an intermediary between the grade data/physical vehicle data and the routing module. This intermediate layer translates physical characteristics into actionable constraints that guide the routing module in making safe decisions, improving reliability without adding computational delay
3Loss of energy
If conventional autonomous vehicle routing systems ignore physical vehicle and route characteristics, then device complexity is reduced, but fuel efficiency deteriorates
Solution Approach 1:
The system pre-identifies grade segments and pre-generates driving constraint data based on physical vehicle data and grade data before the routing decision process. This preliminary processing of physical characteristics allows the system to incorporate detailed physical information without increasing the complexity of the routing module itself, as the constraints are prepared in advance
Solution Approach 2:
The system applies driving constraints specifically to grade segments where they are needed, rather than uniformly across the entire route. By making the routing system adaptive to local physical conditions only where necessary, the system improves fuel efficiency on grade segments without unnecessarily complicating the overall routing architecture
Data Source
AI summary
A method includes identifying map data comprising driving constraint data for a route of an autonomous vehicle (AV), the map data being of a road network associated with the route of the AV, the driving constraint data being based on physical vehicle data. The method further includes, while the AV is travelling the route, identifying current environmental sensing data for a portion of the route. The method further includes causing, based on the map data comprising the driving constraint data for the route and the current environmental sensing data associated with the portion of the route, the AV to travel the portion of the route.