Mobile Object Target State Determination for Collision Avoidance
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Solution Overview
Problem
Existing driving support devices fail to accurately determine a mobile object's target state, as they either focus on statistical driver tendencies without collision considerations or emphasize environmental risks without specifying vehicle movements, leading to inefficient driving support.
Innovation Solution
A mobile object target state determination device that includes a detection section for monitoring a mobile object's position, attitude, and movement, a collision prediction section to forecast collision probabilities, and a determination section to set target states based on pre-specified relationships between object states and collision probabilities, enabling efficient driving support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If driving support devices use statistical driver tendencies from past behaviors, then driving tendencies can be analyzed and reported, but collision possibilities from moment to moment are not considered
Solution Approach 1:
The system segments the driving support function into two independent modules: one that analyzes statistical driver tendencies from past behaviors and another that predicts real-time collision possibilities based on current vehicle state and environmental factors. This segmentation allows both statistical analysis and real-time collision prediction to coexist without interfering with each other, ensuring comprehensive driving support.
Solution Approach 2:
The system introduces an intermediary collision prediction module that acts as a mediator between the statistical driver tendency analysis and the actual driving control. This intermediary processes real-time vehicle state data and environmental information to generate collision probability assessments, which are then integrated with the statistical driving tendency recommendations to provide comprehensive driving support.
2Loss of information
If driving support devices emphasize environmental risks and driver internal states, then environmental risks can be identified, but movement standards and specific vehicle actions are not specified
Solution Approach 1:
The system performs preliminary action by pre-establishing a comprehensive database of movement standards that correlate environmental risk levels with specific vehicle actions. Before actual driving situations arise, the system pre-processes environmental data and driver state information to determine appropriate movement standards, so that when real-time driving occurs, the system can immediately provide clear, pre-calculated guidance on specific vehicle actions to take.
3Reliability
If driving support devices report statistical quantities without considering real-time collision risks, then driver behavior patterns can be analyzed, but moment-to-moment safety guidance is lost
Solution Approach 1:
The system applies parameter changes by dynamically adjusting the weight and influence of statistical driver tendency parameters versus real-time collision prediction parameters based on the current driving situation. When collision risks are high, the system increases the weight of real-time collision prediction parameters; when risks are low, it places more emphasis on statistical driver tendency analysis. This dynamic parameter adjustment ensures both safety and efficiency are optimized for each specific driving context.
Data Source
AI summary
An environmental movement detection section detects the speed of a vehicle and detects a mobile object in the vicinity of the vehicle. A collision probability prediction section predicts the probability of a prospective collision between the vehicle and the detected vicinity mobile object. On the basis of approach speeds, collision probabilities and sideward passing speeds when passing other mobile objects to sideward that have been determined from standard movements, a movement standard learning section learns relationships between approach speed, collision probability and sideward passing speed. On the basis of learning results at the movement standard learning section, a path characteristic point generation section determines a standard sideward passing speed for when passing the vicinity mobile object to sideward that corresponds with the detected approach speed and the predicted collision probability. Thus, standard mobile object states may be determined efficiently.