Vehicle Collision Zone Detection for Smarter Crosswalk Yielding
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Solution Overview
Problem
Traditional collision avoidance systems in vehicles often cause unnecessary yielding, leading to traffic delays by incorrectly assessing collision risks with pedestrians and other agents, particularly in situations where the pedestrian is slowing down or the crosswalk is in a non-crossable state.
Innovation Solution
A vehicle computing system that determines regions of potential collision by analyzing probable paths and velocities of agents and contextual data, using time-space overlap and probability density functions to assess collision risks and decide on actions such as yielding or navigating through the region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional collision avoidance systems identify surfaces and adjust vehicle velocity to avoid collision, then collision avoidance is achieved, but traffic delays occur due to unnecessary yielding
Solution Approach 1:
The system changes the parameters used for collision assessment from simple surface detection to multi-dimensional analysis including probability density functions of agent positions, velocities, and trajectories. This allows the vehicle to distinguish between actual collision risks and non-threatening situations, reducing unnecessary yielding while maintaining collision avoidance reliability
Solution Approach 2:
The system implements feedback by continuously monitoring agent behavior patterns, adjusting probability assessments based on observed trajectories and velocities. This dynamic feedback mechanism enables real-time differentiation between pedestrians who will cross and those who will yield, optimizing both safety and traffic flow
2Object-affected harmful factors
If traditional systems yield to all detected agents in crosswalks, then pedestrian safety is improved, but traffic flow efficiency deteriorates
Solution Approach 1:
The system dynamically adjusts yielding decisions based on real-time probability assessments of agent trajectories. Instead of static yielding rules, the system continuously evaluates whether an agent is likely to cross or yield, enabling adaptive behavior that maintains pedestrian safety while improving traffic flow efficiency through reduced unnecessary stops
3Device complexity
If collision avoidance systems use simple surface detection, then system complexity is reduced, but measurement precision of collision risk deteriorates
Solution Approach 1:
The system transitions from two-dimensional surface detection to multi-dimensional probability space analysis by incorporating agent positions, velocities, trajectories, and behavioral patterns. This dimensional expansion enables precise collision risk assessment while maintaining manageable system complexity through structured probability density function calculations
Data Source
AI summary
Techniques and methods for determining regions. For instance, a vehicle may determine a trajectory of the vehicle and a trajectory of an agent, such as a pedestrian. The vehicle may then determine one or more contextual factors. In some examples, the one or more contextual factors are associated with a location of the agent with respect to a crosswalk, a location of the vehicle with respect to the crosswalk, a state of the crosswalk, and/or the like. The vehicle may then determine the region using the trajectory of the vehicle, the trajectory of the agent, and the one or more contextual factors. Additionally, using a time buffer value and a distance buffer value associated with the region, the vehicle may determine whether to yield to the agent within the region.