Cut-in Prediction Control for Surrounding Vehicles
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Solution Overview
Problem
Existing vehicle control systems, both human-driven and autonomous, struggle to predict and respond to surrounding vehicles' cut-in maneuvers in a timely and effective manner, leading to unpredictable vehicle control.
Innovation Solution
A vehicle equipped with sensors to gather data on surrounding vehicles, such as speed, acceleration, and position, which processes this data to extract features and determine the probability of a cut-in maneuver, allowing for proactive control measures like issuing warnings or adjusting speed to accommodate the surrounding vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the vehicle uses traditional reactive control systems, then the system complexity is low, but the response time to cut-in maneuvers is insufficient and control predictability deteriorates
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring surrounding vehicles and predicting potential cut-in maneuvers before they occur. The probability prediction module calculates the likelihood of cut-in events in advance, allowing the vehicle to prepare appropriate control actions proactively rather than reacting after the cut-in has already happened, thereby improving response speed without requiring overly complex real-time control mechanisms
Solution Approach 2:
The control system dynamically adjusts its behavior based on the predicted probability of cut-in maneuvers. When the probability exceeds a threshold, the system transitions to a more proactive control mode with adjusted parameters; when the probability is low, it maintains normal operating mode. This dynamic adaptation allows the system to optimize response speed only when necessary, balancing performance with system complexity
2Reliability
If the vehicle implements proactive control based on probability prediction, then the reliability of collision avoidance improves, but the computational requirements and processing time increase
Solution Approach 1:
The system continuously collects and pre-processes data from sensors about surrounding vehicles, maintaining ready-to-use information about vehicle positions, speeds, and trajectories. This preliminary data preparation eliminates the need for intensive real-time computation when a cut-in event is detected, reducing processing time while maintaining high collision avoidance reliability through the pre-established probability prediction model
Solution Approach 2:
The probability prediction module uses self-contained algorithms that efficiently process sensor data and generate predictions without requiring complex external computational resources. The system serves its own computational needs through optimized local processing, balancing the trade-off between prediction accuracy and processing time by using lightweight yet effective prediction algorithms
3Measurement precision
If the vehicle monitors and predicts cut-in maneuvers continuously, then the safety and control accuracy improve, but the computational load and energy consumption increase
Solution Approach 1:
The system dynamically adjusts its monitoring and prediction intensity based on the current driving context and detected probability levels. When surrounding vehicles exhibit behaviors indicating high cut-in probability, the system increases monitoring frequency and prediction accuracy; when the situation is stable, it reduces computational intensity. This dynamic approach maintains high measurement precision when needed while minimizing unnecessary energy consumption during normal operation
Solution Approach 2:
The system changes operational parameters such as sensor sampling rates, prediction algorithm complexity, and processing frequency based on the assessed risk level. By adjusting these parameters dynamically, the system optimizes the balance between prediction accuracy and energy consumption, using higher precision only when the predicted probability of cut-in maneuvers warrants it
Data Source
AI summary
Vehicles and methods of predicting a surrounding vehicle cut-in and controlling the vehicle to accommodate the vehicle cut-in are disclosed. In one embodiment, a vehicle includes one or more sensors operable to generate data of a surrounding vehicle, one or more processors, and a non-transitory computer-readable medium storing computer-executable instructions. When the computer-executable instructions are executed by the one or more processors, the one or more processors receive the data of the surrounding vehicle, and extract one or more features from the data of the surrounding vehicle. Based on the one or more features, the one or more processors are controlled to determine a probability that the surrounding vehicle will cut in front of the vehicle, and to control the vehicle in accordance with the probability that the surrounding vehicle will cut in front of the vehicle.