Vehicle Collision Avoidance System with Dynamic Braking Control
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Solution Overview
Problem
Existing collision avoidance and mitigation systems in vehicles fail to adequately account for speeds and behaviors that lead to rear-end and frontal collisions, particularly in environments with autonomous or semi-autonomous vehicles sharing roadways with manually operated vehicles.
Innovation Solution
A collision avoidance and mitigation system equipped with radar sensors, video cameras, and vehicle-to-everything (V2X) transceivers that provide data to a vehicle computer for generating a virtual map of surroundings, enabling the system to detect potential collisions and instruct control units for appropriate maneuvers, such as braking, steering, and suspension adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hard braking is applied to avoid frontal collisions, then frontal collision avoidance is improved, but rear collision severity increases
Solution Approach 1:
The system dynamically adjusts braking force based on real-time analysis of multiple factors including rear vehicle presence, relative speeds, and collision probabilities. Rather than applying fixed hard braking, the system modulates brake pressure to achieve frontal collision avoidance while minimizing rear collision impact through progressive force application.
Solution Approach 2:
The system changes multiple parameters simultaneously including braking force magnitude, braking duration, and deceleration rate based on calculated collision risks. By adjusting these parameters dynamically, the system optimizes the balance between preventing frontal collisions and avoiding excessive rear collision severity.
2Ease of operation
If existing collision avoidance mechanisms are used, then simple collision scenarios are handled, but complex mixed traffic environments with autonomous and manual vehicles are not adequately managed
Solution Approach 1:
The system performs multiple functions through a single integrated platform: detecting vehicles of all types, tracking their behaviors, calculating relative speeds, assessing collision risks, and coordinating braking responses. This multi-functional approach enables the system to handle diverse traffic scenarios including mixed autonomous and manual vehicle environments.
Solution Approach 2:
The system continuously receives feedback from sensors about surrounding vehicles, processes this information to update collision risk assessments, and adjusts braking commands accordingly. This closed-loop feedback mechanism enables adaptive response to changing traffic conditions involving both autonomous and manually operated vehicles.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively reduces the risk and severity of collisions by providing timely alerts and control instructions to avoid or mitigate impending collisions, enhancing safety in mixed traffic environments.
Implementation Method 1
The host vehicle includes a plurality of radar sensors that provide data to the vehicle computer during a driving operation
Data Source
AI summary
Data is collected from vehicle sensors to generate a virtual map of objects proximate to the vehicle. Based on the virtual map, an in-vehicle computer determines a traffic condition in front of and behind a host vehicle. The in-vehicle computer determines collision avoidance maneuvers. The computer instructs vehicle control units to implement the collision avoidance maneuvers. The computer may additionally or alternatively communicate the collision avoidance maneuvers to a driver via an interface. In the case of unavoidable collisions, the computer determines and initiates damage mitigation actions.