Target Vehicle Imitation Control Without V2V Communication
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated driver assistance systems (ADAS) require significant human input for vehicles lacking vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication, limiting their functionality and increasing operator workload.
Innovation Solution
A system and method for ego vehicles to imitate target vehicle behavior using sensors, actuators, and control modules that estimate target vehicle states and trajectories, apply model predictive control (MPC) algorithms, and engage an imitation mode to follow the target vehicle while maintaining safety and performance constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If V2V or V2I communication is implemented to improve ADAS functionality, then automation capability is improved, but device complexity increases
Solution Approach 1:
The system creates a virtual model (copy) of the target vehicle's behavior and dynamics rather than requiring direct communication with it. The ego vehicle replicates the target vehicle's trajectory, speed, and maneuvering patterns through sensor-based observation and mathematical modeling, eliminating the need for V2V/V2I communication infrastructure while achieving similar automation outcomes
Solution Approach 2:
The patent introduces an intermediary computational layer (the behavior imitation system with MPC algorithm) that mediates between sensor inputs and vehicle control outputs. This intermediary processors transform raw sensor data into coordinated actuator commands through optimized path planning, replacing the need for direct vehicle-to-vehicle communication protocols
2Measurement precision
If sensor fusion and filtering algorithms are applied to improve target vehicle state estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The data processing system is segmented into distinct functional modules: sensor data acquisition, fusion algorithms (Kalman/Particle filters), trajectory estimation, and behavior prediction. Each module handles specific aspects of the estimation process independently, making the overall complex system manageable through modular architecture while maintaining high measurement precision
Solution Approach 2:
The system performs preliminary data fusion and filtering operations on sensor inputs before passing processed information to the MPC controller. By pre-processing sensor data through fusion algorithms to estimate target vehicle states in advance, the system reduces the computational burden on downstream control modules while improving estimation accuracy
3Productivity
If model predictive control algorithm is engaged to optimize ego vehicle path, then productivity is improved, but device complexity increases
Solution Approach 1:
The MPC algorithm dynamically adjusts the ego vehicle's path in real-time based on changing target vehicle behavior and environmental conditions. The control parameters (speed, acceleration, steering angle) are continuously optimized over a prediction horizon, allowing the system to adapt to dynamic scenarios while maintaining computational efficiency through receding horizon control
Solution Approach 2:
The system changes key control parameters (velocity, acceleration, steering angle) optimally along the planned path using MPC. By parameterizing the vehicle dynamics and control inputs, the algorithm efficiently computes optimal trajectories that satisfy constraints while maximizing path following performance and imitation accuracy
Data Source
AI summary
A system for imitating target vehicle behavior in automated driving includes sensors capturing ego and target vehicle condition information, actuators selectively altering an ego vehicle state, and control modules. The control modules execute a target vehicle imitating (TVI) application. A first TVI control logic estimates a target vehicle state and trajectory. The ego and target vehicle condition information partially define the target vehicle state and trajectory. A second control logic evaluates target vehicle safety and performance constraints. A third control logic selectively initiates an imitation mode of the ego vehicle based on target and ego vehicle statuses relative to the target vehicle safety and performance constraints. A fourth control logic, models the target vehicle and optimizes a planned ego vehicle path subject to actuator constraints. A fifth control logic generates outputs to the actuators to cause the ego vehicle to follow the planned path and imitate the target vehicle.