Autonomous Vehicle Tracking Point Selection for Companion Navigation

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Solution Overview

Problem

Existing autonomous driving technologies face challenges in effectively navigating environments with companions and obstacles, leading to repetitive movement errors and suboptimal performance.

Innovation Solution

An autonomous driving method utilizing an artificial neural network to predict rewards based on criteria such as accompanied driving, collision avoidance, and energy optimization, allowing the vehicle to select optimal tracking points and adjust its movement strategy dynamically.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If autonomous driving technologies use conventional navigation methods, then the vehicle can operate in environments with companions and obstacles, but repetitive movement errors occur and performance is suboptimal

Engineering Contradiction:

Improvedriving performanceVSAvoidmovement efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent implements a feedback mechanism where the autonomous vehicle continuously monitors its movement state, companion position, and obstacle locations, then adjusts its tracking point selection and movement strategy based on this feedback. This closed-loop control system enables the vehicle to learn from past movements and correct repetitive errors, improving both reliability and productivity in accompanied driving scenarios

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts the tracking point selection and movement strategy based on real-time environmental conditions. Rather than using fixed navigation rules, the vehicle adapts its behavior by selecting optimal tracking points from multiple candidates and adjusting its movement approach based on companion actions and obstacle positions, thereby resolving the contradiction between reliable performance and movement efficiency

Inventive Principle:

Principle #15Dynamics

2Reliability

If the autonomous vehicle closely tracks the companion, then accompanied driving performance improves, but collision risk with obstacles increases

Engineering Contradiction:

Improveaccompanied driving performanceVSAvoidcollision risk

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent segments the tracking task by defining multiple candidate tracking points around the companion rather than a single fixed position. This segmentation allows the system to select different tracking points based on obstacle locations, maintaining close companionship while avoiding collisions. The spatial segmentation of tracking points enables simultaneous optimization of accompanied driving performance and collision avoidance

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies local quality by selecting tracking points with different spatial characteristics based on the local environment. When obstacles are present in certain directions, the system chooses tracking points in safer local regions, thereby maintaining effective companionship while reducing collision risk in specific spatial zones

Inventive Principle:

Principle #3Local quality

3Adaptability or versatility

If the autonomous vehicle maintains flexible movement strategies, then adaptability to different situations improves, but computational complexity increases

Engineering Contradiction:

Improvemovement strategy adaptabilityVSAvoidcomputational complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by pre-defining multiple candidate tracking points around the companion before actual driving occurs. This preparation reduces real-time computational complexity because the system only needs to select from pre-established candidates rather than generate new trajectories from scratch. The preliminary setup enables flexible adaptability while managing computational demands

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses partial action by selecting from a subset of candidate tracking points rather than evaluating all possible movement strategies. This selective approach provides sufficient adaptability for various situations while reducing computational complexity by focusing on the most relevant tracking point candidates

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11579619B2Autonomous driving methods and apparatuses

Publication Date: 2023.02.14 SAMSUNG ELECTRONICS CO LTD

AI summary

An autonomous driving apparatus for accompanied driving in an environment that includes a companion and an obstacle includes a sensor, processing circuitry, and a driver. The sensor may generate sensor data. The processing circuitry may define a current state of the autonomous driving apparatus based on processing the sensor data to determine respective positions of the companion and the obstacle in the environment and select a first tracking point of a plurality of tracking points at least partially surrounding the position of the companion in the environment based on the current state, a position of each tracking point of the plurality of tracking points in the environment defined by the position of the companion in the environment. The driving apparatus drive mechanism may move the autonomous driving apparatus to the first tracking point to cause the autonomous driving apparatus to accompany the companion in the environment.