Traffic Jam Assist Activation via Vehicle Acceleration Data
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Solution Overview
Problem
Existing Automated Traffic Jam Assist (TJA) systems lack the ability to be activated autonomously and accurately detect traffic jams, relying on human input and suffering from inaccuracies in real-time traffic data prediction.
Innovation Solution
A method and system that utilize acceleration data from vehicle components, combined with remote traffic data and vehicle proximity detection, to autonomously activate TJA systems, enabling semi-autonomous control of steering, braking, and propulsion to navigate through traffic jams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing TJA systems require human input for activation, then the system can be activated, but driver distraction increases and activation may not be timely
Solution Approach 1:
The TJA system automatically detects traffic jam conditions and activates itself without requiring driver input. The system monitors vehicle sensors, mapping data, and traffic information to autonomously determine when to engage TJA, eliminating the need for manual activation and reducing driver distraction.
Solution Approach 2:
The system continuously monitors traffic conditions and vehicle sensors in advance to predict traffic jam scenarios before they fully develop. By detecting patterns in acceleration data, vehicle proximity, and traffic reports, the system prepares for and activates TJA proactively, ensuring timely engagement before the situation deteriorates.
2Measurement precision
If mapping applications with real-time traffic data are used to predict traffic jams, then traffic jam detection is provided, but accuracy and timeliness are insufficient
Solution Approach 1:
The system combines multiple data sources including vehicle sensor data (acceleration, braking, steering), GPS location, mapping application traffic data, and vehicle-to-vehicle communication information. This multi-source integration cross-validates traffic jam detection, significantly improving accuracy and timeliness compared to relying on mapping applications alone.
Solution Approach 2:
The system continuously monitors traffic conditions and compares actual vehicle behavior data against predicted traffic patterns. When discrepancies are detected or traffic conditions worsen, the system adjusts its detection thresholds and activates TJA accordingly. This feedback loop ensures accurate and timely detection by adapting to real-time conditions.
3Productivity
If vehicles operate in traffic jams for extended periods, then navigation through congestion is achieved, but fuel economy decreases and collision risk increases
Solution Approach 1:
The TJA system dynamically adjusts vehicle operations based on real-time traffic conditions, continuously monitoring the behavior of surrounding vehicles and adapting acceleration, deceleration, and lane change decisions. This dynamic response optimizes fuel consumption by avoiding unnecessary engine operations while maintaining safe navigation through congested traffic.
Solution Approach 2:
The system converts the harmful effects of traffic jam operation (fuel waste, driver fatigue, collision risk) into benefits by implementing automated monitoring and control. The automated system reduces fuel consumption through optimized vehicle operations, maintains safer following distances to prevent collisions, and eliminates driver fatigue by handling the monotonous traffic jam navigation tasks.
Data Source
AI summary
Acceleration data is received for each of a plurality of times t1 . . . tn, from one or more components in a host vehicle. A traffic jam assist (TJA) system is based on the acceleration data, thereby actuating one or more of steering, braking, and propulsion.