Adaptive Vehicle Collision Avoidance With Harm-Minimizing Control
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Solution Overview
Problem
Existing collision-avoidance systems fail to account for individual driver skills, provide one-size-fits-all solutions, and do not effectively manage post-collision hazards, often leading to increased damage and injuries when collisions are unavoidable.
Innovation Solution
A vehicle collision avoidance and harm minimization system that utilizes sensors and processors to analyze imminent collisions, calculate sequences of acceleration, braking, and steering to either avoid collisions or minimize their impact, adjusting interventions in real-time based on driver capabilities and changing circumstances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic braking is applied rapidly to avoid collision, then collision avoidance capability is improved, but vehicle stability deteriorates due to skidding
Solution Approach 1:
The system dynamically adjusts braking intensity based on real-time vehicle state and environmental conditions. The processor continuously monitors vehicle speed, distance to obstacle, road surface conditions, and driver behavior patterns to modulate braking force, transitioning from static one-size-fits-all braking to dynamic adaptive braking that maintains vehicle stability while achieving collision avoidance.
Solution Approach 2:
The system changes multiple parameters simultaneously including braking force magnitude, braking duration, brake application pattern (progressive vs. abrupt), and wheel differential braking intensity. These parameter adjustments are optimized based on detected conditions such as road friction coefficients, vehicle load, and proximity to obstacles, allowing the system to balance collision avoidance with skid prevention.
2Device complexity
If one-size-fits-all collision avoidance intervention is applied, then system complexity is reduced, but adaptability to individual driver skills deteriorates
Solution Approach 1:
The system automatically characterizes driver skill level through passive observation of driving patterns, reaction times, and manual intervention preferences without requiring explicit driver input or system configuration. The processor analyzes steering corrections, acceleration patterns, and brake application characteristics to build a driver profile, enabling adaptive collision avoidance that automatically adjusts to each driver's capabilities and preferences.
Solution Approach 2:
The system implements continuous feedback loops where sensor data about driver behavior, vehicle state, and collision scenarios are fed back to the processor, which adjusts future intervention strategies accordingly. This feedback mechanism allows the system to learn from each driving episode and refine its collision avoidance approach to match the specific driver's skills and preferences over time.
3Reliability
If strong evasive action is taken to avoid side-encroachment collision, then collision avoidance capability is improved, but vehicle control stability deteriorates due to skidding
Solution Approach 1:
The system dynamically coordinates steering angle, braking force, and acceleration modulation during evasive maneuvers. Rather than applying fixed steering angles or brake forces, the processor continuously adjusts these parameters based on real-time feedback from sensors monitoring vehicle dynamics, road conditions, and obstacle position, enabling smooth adaptive evasive actions that maintain traction and prevent skidding while achieving collision avoidance.
Solution Approach 2:
The system applies counteracting forces to balance the aggressive steering and braking required for evasive maneuvers. When strong evasive action is initiated, the system applies opposing forces through differential wheel braking and controlled acceleration to counteract the destabilizing effects of rapid steering changes, preventing vehicle spin and maintaining directional control throughout the evasive maneuver.
4Reliability
If automatic system applies brakes aggressively to create space, then collision avoidance capability is improved, but following driver's reaction time deteriorates
Solution Approach 1:
The system uses feedback from rear-mounted sensors detecting following vehicle proximity and speed to modulate braking intensity. When a following vehicle is detected at close range, the system reduces braking aggressiveness or extends braking duration to allow the following driver adequate reaction time, while still achieving sufficient space creation through coordinated vehicle control strategies.
Solution Approach 2:
The system dynamically adjusts braking strategy based on real-time detection of following traffic conditions. Rather than applying fixed aggressive braking, the processor modulates brake force in response to following vehicle presence, using progressive braking patterns that balance space creation with providing adequate warning to following drivers, thereby maintaining overall traffic safety.
Data Source
AI summary
A subject vehicle in traffic can detect a second vehicle using sensors that measure motions of the second vehicle, and thereby determine one or more future trajectories of the second vehicle, and thereby determine whether a collision between the subject and second vehicles is imminent. The subject vehicle can calculate one or more sequences of actions, each action comprising an acceleration, a deceleration, and/or a steering action of the subject vehicle. The subject vehicle can also calculate whether any of the one or more sequences of actions can avoid the imminent collision, and also calculate an expected harm of the imminent collision according to each of the one or more sequences of actions. The subject vehicle can then autonomously select and implement a particular sequence of actions that is calculated to avoid the imminent collision or to minimize the harm of the imminent collision.