Distributed Vehicle Path Planning With Cloud-Offloaded Computing

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Solution Overview

Problem

Current semi-autonomous and autonomous driving systems require high computational power, leading to expensive and power-intensive equipment, which can be inefficient and costly to maintain and upgrade.

Innovation Solution

A distributed computing system that offloads computational load from the vehicle to a cloud computing device, using a vehicle computing device to transmit navigation information and receive path planning information, allowing for reduced power consumption and easier retrofitting of existing systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If high computational power is implemented inside the vehicle using expensive and high-power equipment, then autonomous driving capability is achieved, but power consumption and cost increase significantly

Engineering Contradiction:

Improveautonomous driving capabilityVSAvoidpower consumption

Core Design Contradiction:

Extent of automationVSUse of energy by moving object

Solution Approach 1:

The system divides computational tasks into two segments: path planning (less time-critical) is performed in the cloud, while trajectory control (time-critical) is handled by the vehicle's onboard computer. This segmentation allows the vehicle to achieve autonomous driving capability with reduced onboard computational requirements and lower power consumption.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A communication interface acts as an intermediary between the cloud computing system and the vehicle's onboard computer. This intermediary enables the vehicle to leverage external cloud computing resources for path planning while maintaining local control for time-critical decisions, thereby reducing the need for high-power onboard equipment.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Extent of automation

If high computational power is implemented inside the vehicle, then autonomous driving capability is achieved, but equipment cost increases

Engineering Contradiction:

Improveautonomous driving capabilityVSAvoidequipment cost

Core Design Contradiction:

Extent of automationVSEase of manufacture

Solution Approach 1:

The system extracts the path planning computational function from the vehicle and relocates it to the cloud. This extraction allows the vehicle to achieve autonomous driving capability without investing in expensive high-power onboard computing equipment, as the cloud provides the necessary computational resources.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of duplicating high-power computing equipment in every vehicle, the system uses a centralized cloud computing infrastructure that serves multiple vehicles. This copying approach at the infrastructure level reduces per-vehicle equipment costs while maintaining autonomous driving capability.

Inventive Principle:

Principle #26Copying

3Use of energy by moving object

If all path planning is performed in the cloud, then onboard power consumption is reduced, but response time may increase due to communication latency

Engineering Contradiction:

Improveonboard power consumptionVSAvoidresponse time

Core Design Contradiction:

Use of energy by moving objectVSLoss of time

Solution Approach 1:

The system applies local quality by placing different computational functions at different locations: path planning (less time-sensitive) is performed remotely in the cloud, while trajectory control (highly time-sensitive) is executed locally on the onboard computer. This spatial distribution of computational tasks optimizes both power consumption and response time.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The cloud computing system performs path planning in advance and sends the planned path to the vehicle before the vehicle needs to execute it. This preliminary action allows the onboard computer to focus on real-time trajectory control without the computational burden of path planning, reducing both power consumption and response time.

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If cloud computing is used for path planning, then system upgradeability is improved, but system complexity increases

Engineering Contradiction:

Improvesystem upgradeabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The cloud computing infrastructure serves as a universal platform that can provide computational resources to multiple vehicles and potentially other applications. This multi-functionality improves system upgradeability, as the cloud can be updated and improved independently of individual vehicles, while the added communication interface complexity is offset by the versatility gained.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11807266B2Driving system for distribution of planning and control functionality between vehicle device and cloud computing device, vehicle computing device, and cloud computing device

Publication Date: 2023.11.07 MITSUBISHI ELECTRIC CORP

AI summary

A driving system includes a vehicle computing device on a vehicle and a cloud computing device at a location physically separated from the vehicle. The vehicle computing device transmits navigation information to the cloud computing device, including destination information, location information of the vehicle, and high-definition map information regarding a surrounding environment of the vehicle. The cloud computing device generates, based on the navigation information, path information including primary path information and backup path information and transmits the path information to the vehicle computing device. The vehicle computing device controls a trajectory of the vehicle based on the received path information.