Autonomous Vehicle Path Planning for Risky Neighbor Behavior
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating safely around non-autonomous vehicles that do not follow traffic laws and practices, posing risks due to unpredictable behaviors such as rapid acceleration, lane changes without signaling, and occupying multiple lanes.
Innovation Solution
A system and method for autonomous vehicles that utilize sensors (lidar, radar, and cameras) to detect neighboring vehicles, recognize behaviors, calculate risk factors, and plan paths to avoid potential hazards, incorporating risk-related clusters to predict future behaviors and adjust vehicle navigation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous vehicle uses standard GPS-based navigation to follow a calculated path, then the vehicle can efficiently reach its destination, but it cannot adequately respond to unpredictable risky behaviors of non-autonomous vehicles
Solution Approach 1:
The system performs preliminary risk assessment by continuously monitoring neighboring vehicles and calculating risk factors before conflicts occur. It identifies risky behaviors such as lane changes without signaling, rapid acceleration, and occupying multiple lanes, then proactively adjusts the autonomous vehicle's path planning to avoid potential hazards rather than reacting after incidents occur.
Solution Approach 2:
The system implements continuous feedback by monitoring the behaviors of non-autonomous vehicles and updating risk factors in real-time. Sensors detect actions like blind-spot indicator illumination, and the system responds by adjusting the autonomous vehicle's position or speed, creating a closed-loop control system that adapts to changing traffic conditions.
2Reliability
If the autonomous vehicle maintains a conservative distance from all neighboring vehicles to ensure safety, then collision risk is reduced, but navigation efficiency and travel time increase
Solution Approach 1:
The system applies different safety margins to different neighboring vehicles based on their individual risk profiles. Vehicles exhibiting risky behaviors such as lane changes without signaling or occupying multiple lanes are assigned larger safety buffers, while normal vehicles receive standard spacing. This localized differentiation optimizes both safety and efficiency by not overly conserving distance with all vehicles.
Solution Approach 2:
The system dynamically adjusts the safety distance parameter based on the calculated risk factor of each neighboring vehicle. When a vehicle exhibits risky behavior, the system increases the safety margin; when vehicles behave normally, the system reduces the margin to improve flow. This parameter adaptation allows the vehicle to balance safety and efficiency continuously.
Data Source
AI summary
A method includes receiving perception images of an area surrounding the vehicle with at least one sensor and detecting at least one perception task from the perception images with the at least one perception task identifying at least one neighboring vehicle. The method calculates a risk factor for the at least one neighboring vehicle by recognizing a behavior of the at least one neighboring vehicle from the perception images and plans the path for the vehicle based on the at least one perception task and the risk factor. The path is then executed for the vehicle.