Autonomous Driving Pedestrian Prediction Using Segmented Velocity Components
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Solution Overview
Problem
Existing autonomous driving systems struggle to accurately predict the movement direction of pedestrians, leading to potential safety accidents due to sudden changes in pedestrian heading, which affects the timely adjustment of driving strategies.
Innovation Solution
The method involves obtaining movement data, including velocity components along the lane's width direction and duration of pedestrian presence, to determine the pedestrian's movement direction using a decision tree model, enabling the autonomous vehicle to generate a driving strategy that adjusts velocity and direction for safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If heading information and velocity information are used to predict pedestrian movement, then the prediction process is simple, but the prediction accuracy deteriorates due to sudden changes in pedestrian heading
Solution Approach 1:
The pedestrian movement prediction is segmented into two independent components: width direction velocity (affecting lane changing) and length direction velocity (affecting forward movement). This segmentation allows each component to be predicted separately using appropriate features, improving overall accuracy without excessive complexity
Solution Approach 2:
The prediction model transitions from using only temporal features (heading and velocity) to incorporating spatial dimension features (pedestrian position relative to lane, lane width). This dimensional expansion enables the model to capture the geometric context of pedestrian movement, significantly improving prediction accuracy
2Power
If traditional prediction methods are used, then the computational load is low, but the autonomous vehicle cannot adjust driving strategy in time, resulting in safety accidents
Solution Approach 1:
The method extracts only the critical features needed for prediction: width direction velocity component and pedestrian position relative to lane. By extracting only the most relevant features rather than processing all available data, the computational load is reduced while maintaining high prediction accuracy and safety
Solution Approach 2:
The system performs preliminary prediction of pedestrian movement direction before the pedestrian actually enters the vehicle's path. This advance prediction allows the autonomous vehicle to prepare and execute avoidance maneuvers in time, ensuring safety without requiring excessive computational power during critical moments
Data Source
AI summary
The present application discloses a method and an apparatus for autonomous driving control, an electronic device, and a storage medium; the application relates to the technical field of autonomous driving. A specific implementation solution is: obtaining movement data of a pedestrian, where the movement data includes a velocity component of the pedestrian along a width direction of a lane and a time of duration that the pedestrian cuts into a driving path of the autonomous vehicle from one side; determining a movement direction of the pedestrian according to the movement data and the movement information of the pedestrian; and generating a driving strategy for the autonomous vehicle according to the movement direction of the pedestrian. Therefore, the movement direction of the pedestrian can be accurately predicted, which facilitates the autonomous vehicle to avoid the pedestrian and insures driving safety.