Autonomous Vehicle Trajectory Latency Compensation
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Solution Overview
Problem
Autonomous vehicles face challenges in determining trajectories to avoid collisions due to system latency, which can lead to inaccurate predictions of object behavior and increased safety risks, especially in environments with multiple dynamic objects.
Innovation Solution
The system predicts future latency and uses this prediction, along with a predetermined acceleration, to determine a safety distance around objects, allowing the vehicle to yield or circumvent them more safely and efficiently by accounting for potential future movement and acceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses sensor data to predict object behavior and generate trajectories in real-time, then the vehicle can respond to dynamic objects and avoid collisions, but system latency causes delays in processing and executing trajectory decisions, reducing responsiveness and safety
Solution Approach 1:
The system performs preliminary actions by predicting object behavior and generating trajectory decisions in advance, before the actual execution time. By anticipating future object positions and preparing trajectory options ahead of time, the system compensates for processing delays and ensures timely response when latency occurs
Solution Approach 2:
The system applies beforehand cushioning by incorporating safety margins and uncertainty buffers into trajectory predictions. It accounts for potential object movements and system delays by generating conservative trajectory options that maintain safe distances, cushioning against the adverse effects of latency-induced timing errors
2Reliability
If the autonomous vehicle accounts for system latency by predicting future object locations, then collision avoidance improves, but the complexity of processing and calculating trajectories increases
Solution Approach 1:
The system applies parameter changes by transforming the trajectory prediction problem into a parameter optimization task. It adjusts key parameters such as prediction time horizons, safety margins, and object velocity estimates to balance accuracy requirements with computational feasibility, simplifying the overall processing complexity
Solution Approach 2:
The system uses segmentation by dividing the trajectory prediction task into discrete time steps and separate processing stages. It breaks down complex environment modeling into individual object predictions, then combines them sequentially, reducing the computational burden compared to holistic scene analysis
3Reliability
If the autonomous vehicle generates multiple trajectory options to account for object acceleration and latency, then safety decisions become more robust, but the processing time and computational resources required increase
Solution Approach 1:
The system applies partial action by generating a limited set of critical trajectory options rather than exhaustively evaluating all possible paths. It focuses computational resources on generating a small number of high-probability safe trajectories that account for latency and acceleration, rather than producing numerous marginal alternatives
Solution Approach 2:
The system uses dynamics by making trajectory generation adaptive and flexible. It dynamically adjusts the number and complexity of trajectory options based on real-time factors such as object velocity, distance, and environmental uncertainty, optimizing processing efficiency while maintaining robust safety decisions
Data Source
AI summary
The described techniques relate to modifying a trajectory of a vehicle, such as an autonomous vehicle, based on a latency associated with one or more systems of the vehicle. In examples, a planning system of the vehicle may predict a future latency (e.g., based on an interval between receipt of sensor data and/or object predictions), and use the future latency to determine a time at which to predict object behavior. Additionally, in some cases, the described techniques may include associating a predetermined acceleration with a predicted future location of the object to create a safety distance around the object, where the predetermined acceleration may be based on a maximum expected acceleration of the object. The safety distance may account for the object potentially accelerating in one or more directions at the future time.