Autonomous Driving Route Constraints for Grade-Aware Braking
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Solution Overview
Problem
Conventional autonomous vehicle routing modules lack the ability to consider long-term route segments, leading to inappropriate vehicle actions such as premature brake wear and potential collisions, due to their limited awareness of road grades and vehicle capabilities.
Innovation Solution
Implementing a physics-informed optimization system that generates driving constraint data based on route grade and vehicle physical data, allowing the routing module to generate short-time horizon routing data that accounts for specific braking strategies, calculated maximum speeds, and gear ratios for each segment of the route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional routing modules use limited short-time horizon routing data, then processing speed is maintained, but vehicle component wear increases and collision risk rises due to lack of long-term route awareness
Solution Approach 1:
The patent divides the route into multiple segments based on road grade characteristics (uphill, downhill, flat portions). The routing module processes each segment separately using appropriate driving strategies, allowing long-term route awareness without overwhelming computational complexity. Each segment can be evaluated independently with its own constraint data, enabling reliable collision avoidance through systematic breakdown of the overall route.
Solution Approach 2:
The system generates driving constraint data in advance for each route segment before the vehicle actually traverses it. This preliminary action includes pre-calculating appropriate speeds, braking strategies, and gear selections based on grade data and vehicle capabilities. By preparing this information beforehand, the routing module can make reliable decisions without real-time computational overload, improving both safety and component protection.
2Loss of energy
If the routing module considers long-term route segments with grade data, then component wear and fuel consumption improve, but processing overhead increases
Solution Approach 1:
The patent applies different driving strategies and constraint levels to different route segments based on their specific characteristics. Uphill segments receive different speed and power constraints than downhill or flat segments. This local quality approach allows the system to optimize fuel efficiency where needed (on steep grades) without unnecessarily complicating the routing logic for all segments, thereby reducing overall processor overhead while maintaining energy efficiency.
Solution Approach 2:
The system dynamically adjusts driving parameters (speed limits, acceleration rates, braking force) based on the specific grade characteristics of each route segment. By changing these parameters according to local conditions rather than using fixed conservative limits throughout, the routing module achieves better fuel efficiency on challenging terrain while avoiding excessive computational processing in flat, low-risk areas.
3Reliability
If driving constraint data is generated based on grade data and vehicle physics, then inappropriate vehicle actions are prevented, but data processing requirements increase
Solution Approach 1:
The patent extracts only the essential grade data and vehicle capability parameters needed for constraint generation, rather than processing all available route and vehicle information. By taking out only the critical elements (grade percentages, vehicle mass, braking capacity), the system generates sufficient driving constraint data to prevent inappropriate actions and excessive brake wear without the need to process overwhelming volumes of ancillary data.
Data Source
AI summary
A method includes identifying, based on grade data of a route of an autonomous vehicle (AV), a segment of the route that has a grade value that meets a threshold grade value. Responsive to identifying the segment, the method further includes generating, based on the grade data and physical vehicle data of the AV, driving constraint data for the segment of the route. The method further includes causing a routing module of the AV to generate, based on the driving constraint data for the segment of the route, short time horizon routing data corresponding to a portion of the segment. The AV is to travel the portion of the segment of the route based on the short time horizon routing data.