Vehicle Behavior Planning for Overtaking Maneuvers
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Solution Overview
Problem
Autonomous or semi-autonomous vehicles face challenges in identifying and avoiding undertaking maneuvers, particularly when encountering larger vehicles, due to increased blind spots and unexpected nature of such maneuvers, which can lead to safety risks.
Innovation Solution
The system determines the driving context by analyzing sensor data to identify nearby vehicles and their states, generating a trajectory that avoids undertaking maneuvers by considering the vehicle class and lane change intentions, thereby adjusting the path to prevent collisions and ensure safe overtaking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the ego vehicle maintains its current trajectory to overtake the nearby vehicle, then the overtaking maneuver can be completed efficiently, but the risk of undertaking maneuver increases due to blind spots and unexpected situations
Solution Approach 1:
The system performs preliminary detection of the nearby vehicle's state (particularly whether it is a large vehicle and if its turn signal is active) before executing the overtaking maneuver. This advance assessment allows the ego vehicle to predict potential undertaking scenarios and adjust its trajectory proactively, rather than reacting after the hazard is realized
Solution Approach 2:
The system continuously monitors the nearby vehicle's turn signal state and adjusts the ego vehicle's trajectory in real-time based on this feedback. When the turn signal is detected as active, the system modifies the overtaking path to maintain safe distance, creating a closed-loop control system that adapts to changing conditions
2Reliability
If the ego vehicle adjusts its trajectory to avoid undertaking maneuver, then safety risk is reduced, but the overtaking time and distance increase
Solution Approach 1:
The system applies different trajectory adjustment strategies based on the local situation - specifically whether the nearby vehicle is a large vehicle and whether its turn signal is active. Instead of uniformly slowing down or extending the overtaking distance in all cases, the system selectively adjusts the trajectory only when the merge threshold is satisfied, maintaining efficient overtaking in safer conditions
3Measurement precision
If the system continuously monitors nearby vehicles and their states, then the accuracy of identifying undertaking risks improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The system extracts only the critical features needed for undertaking risk assessment from the sensor data - specifically focusing on whether the nearby vehicle is a large vehicle (class 4 or larger) and whether its turn signal is active. By filtering out unnecessary information and concentrating on these key parameters, the system achieves high detection accuracy without requiring complex processing of all possible vehicle attributes
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to improving vehicle behavior planning to avoid undertaking maneuvers. In one embodiment, a method includes generating a driving context from sensor data about a surrounding environment of an ego vehicle. The driving context identifying lanes of a roadway and a position of the ego vehicle in the lanes. The method includes, in response to determining that the driving context and a state of a nearby vehicle satisfy a merge threshold, generating a trajectory for the ego vehicle that avoids undertaking the nearby vehicle. The method includes controlling the ego vehicle according to the trajectory.