Autonomous Vehicle Object Detection Using Adaptive Visual Prompts
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Solution Overview
Problem
Autonomous vehicles face challenges in object detection due to rapidly changing environmental conditions and limited computational capacity, particularly in scenarios like nighttime, bad weather, or tunnels.
Innovation Solution
A method and apparatus that utilize a computing device equipped with a processor and memory to obtain driving images and environmental data, receive trained visual prompts from a server based on the images and data, and perform object detection by merging the images with the prompts using a neural network model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection is performed using traditional methods in rapidly changing environmental conditions, then the system can operate with existing computational resources, but the detection accuracy deteriorates in challenging environments such as nighttime, bad weather, or tunnels
Solution Approach 1:
The system performs preliminary classification of environmental conditions (time of day, weather, tunnel status) before object detection. Based on the classified environment, appropriate visual prompts are pre-selected and applied to enhance the driving image. This preliminary adaptation allows the detection model to maintain high accuracy across diverse environmental conditions without requiring complete retraining for each scenario.
Solution Approach 2:
The system dynamically changes the visual prompt parameters based on environmental classification. Different environmental conditions trigger different visual prompt applications (e.g., nighttime prompts for low light, weather-specific prompts for rain or fog). This parameter adaptation enables the detection system to optimize its performance for each specific environmental context while using the same base neural network model.
2Measurement precision
If the computational capacity of the autonomous vehicle is increased to improve object detection in all conditions, then detection accuracy improves, but the device complexity and cost increase
Solution Approach 1:
The system introduces visual prompts as an intermediary element between the driving image and the object detection model. These prompts are lightweight visual cues that are added to the image based on environmental conditions. Instead of requiring a complex, heavily trained model for each environment, the simple prompt addition mechanism provides environmental adaptation with minimal computational overhead, maintaining accuracy while avoiding increased device complexity.
Solution Approach 2:
The object detection system is segmented into distinct functional components: environmental condition classification, visual prompt selection/application, and object detection. This segmentation allows the system to use a lightweight detection model while compensating for environmental variations through the intermediate prompt application step. Each component performs a specific function, avoiding the need for a single complex model that would require significant computational resources.
3Measurement precision
If environmental data collection and processing is performed to enable adaptive visual prompts, then object detection accuracy in diverse conditions improves, but the processing time and system complexity increase
Solution Approach 1:
Environmental conditions are classified into predefined categories (time of day, weather types, tunnel status) before the detection process. This preliminary classification organizes environmental data into discrete, manageable categories that can be quickly mapped to corresponding visual prompts. By pre-defining these categories and their associated prompts, the system avoids complex real-time analysis during detection, reducing processing time while maintaining adaptability.
Solution Approach 2:
The system changes discrete parameters (environmental category classifications) rather than continuously processing raw environmental data. Each environmental parameter is classified into specific categories that directly correspond to pre-selected visual prompts. This parameter discretization enables rapid decision-making for prompt selection without requiring extensive processing of continuous environmental variables, thus minimizing time loss.
Data Source
AI summary
Provided are an object detection method and apparatus for an autonomous vehicle. A method of controlling a vehicle includes: detecting a surrounding environment using pieces of data on a driving environment of the vehicle and generating an indication of the surrounding environment; determining, among trained visual prompts received via a network from a server, a target visual prompt corresponding to the pieces of data; generating a merged image by combining a driving image of the autonomous vehicle with the target visual prompt using a predetermined operation; and performing object detection by inputting the merged image into a neural network model of the vehicle, the neural network model configured to infer objects from images inputted thereto.