Real-Time Vehicle Path Planning for Dangerous Cargo Avoidance
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Solution Overview
Problem
Special vehicles with dangerous cargo pose a risk of accidents when sharing traffic lanes with consumer vehicles due to inadequate path planning systems.
Innovation Solution
A path planning system using in-vehicle sensors, cameras, and GPS to collect data, analyze road and traffic flow data, and identify and avoid vehicles with specific functions like autonomous driving, to optimize driving paths and enhance safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If special vehicles with dangerous cargo share traffic lanes with consumer vehicles, then traffic lane utilization is improved, but safety is deteriorated
Solution Approach 1:
The path planning system segments the traffic environment by identifying and classifying different vehicle types (special vehicles with dangerous cargo vs. consumer vehicles) using sensor data and image recognition. This segmentation allows the system to treat different vehicle categories differently in path planning, enabling safer routing decisions while maintaining overall traffic lane utilization.
Solution Approach 2:
The system performs preliminary identification and classification of special vehicles with dangerous cargo before path planning is finalized. By detecting these vehicles in advance using sensors and image processing, the system can pre-calculate alternative routes that avoid potential hazards, thereby improving safety without compromising traffic efficiency.
2Reliability
If path planning system identifies and avoids vehicles with specific functions, then safety is improved, but path planning complexity is deteriorated
Solution Approach 1:
The path planning system employs a multi-functional architecture that integrates sensor data acquisition, image recognition, vehicle classification, and path optimization into a unified platform. This universal system handles multiple tasks simultaneously, improving safety through comprehensive vehicle identification while managing complexity through integrated design rather than separate systems.
Solution Approach 2:
The system introduces an intermediary layer (image recognition module and vehicle classification module) between raw sensor data and path planning algorithms. This intermediary processes and filters data to identify special vehicles with dangerous cargo, presenting simplified, processed information to the path planning algorithm, thereby reducing its complexity burden.
3Reliability
If real-time data collection and analysis is performed to identify dangerous vehicles, then safety is improved, but computational resource consumption is deteriorated
Solution Approach 1:
The system applies partial action by focusing computational resources on identifying and analyzing only those vehicles that pose potential risks (special vehicles with dangerous cargo) rather than processing all vehicles equally. Image recognition and detailed analysis are applied selectively to suspicious targets, reducing overall computational resource consumption while maintaining safety.
Solution Approach 2:
The system implements feedback mechanisms where sensor data and image recognition results continuously inform path planning decisions in real-time. This feedback loop allows the system to adapt dynamically, concentrating computational effort on relevant hazards as they are detected, thereby improving safety response efficiency while optimizing resource usage through demand-driven processing.
Data Source
AI summary
A path planning method applied to a vehicle. The path planning method comprises obtaining a current location of the vehicle in real time, determining road data and traffic flow data matched with the current location of the vehicle, determining an objective vehicle according to the vehicle identify information, obtaining the vehicle location of the objective vehicle, and obtaining an objective driving location of the vehicle and determining a planned driving path of the vehicle based on the vehicle location of the objective vehicle, the current location, and the objective driving location of the vehicle. An electronic device and a non-transitory storage are also disclosed.