AI Dynamic Routing for Battery-Aware Autonomous Driving
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Solution Overview
Problem
Conventional autonomous driving vehicles do not account for power consumption when navigating, leading to reduced operational range due to high electrical demand from sensors and computing power, which can deplete battery energy quickly.
Innovation Solution
A dynamic routing algorithm utilizing artificial intelligence and improved memory management is implemented in an autonomous driving vehicle (ADV) to optimize navigation routes based on real-time power requirements, sensor data, and battery state, allowing the vehicle to adjust routes to conserve energy and extend operational range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional routing algorithms are used without power consumption consideration, then navigation simplicity is maintained, but operational range is reduced due to high electrical demand
Solution Approach 1:
The routing algorithm dynamically adjusts navigation paths based on real-time power consumption data from sensors and computing operations. The system continuously monitors electrical demand and modifies routing decisions to optimize energy usage, transforming a static routing problem into a dynamic power-aware optimization process
Solution Approach 2:
The system implements feedback loops that monitor power consumption metrics from sensors, processors, and other vehicle systems. This feedback information is fed back into the routing algorithm to continuously refine navigation decisions, ensuring optimal power management while maintaining navigation effectiveness
2Measurement precision
If high-power sensors and computing systems are deployed for autonomous navigation, then navigation accuracy is improved, but battery energy is depleted quickly
Solution Approach 1:
The system changes operational parameters of sensors and computing systems based on navigation requirements and power availability. It dynamically adjusts sensor activation states, processing intensity, and data collection frequency to maintain necessary navigation accuracy while optimizing power consumption for extended operational duration
Solution Approach 2:
The system applies partial action by selectively activating only the necessary subset of sensors and computing resources required for current navigation conditions. Rather than running all systems at full capacity continuously, it employs just enough computational power and sensor activity to maintain safe and accurate autonomous operation
3Use of energy by moving object
If real-time power management is implemented, then operational range is extended, but system complexity increases
Solution Approach 1:
The power management system is integrated into the existing autonomous vehicle control architecture, allowing the same computational resources to serve multiple functions: navigation decision-making, power consumption monitoring, and routing optimization. This multi-functionality reduces overall system complexity despite the added power management capabilities
Data Source
AI summary
The invention relates to a system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.