Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicentre of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output, and ranks them according to likelihood. To verify the method's performance and generalizability, we present a real-world dataset that consists of over 23,000 vehicles traversing five different intersections, collected using a vehicle mounted Lidar based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.
Very recently there has been a focus on using deep learning to solve this problem, namely using recurrent neural networks (RNNs). This network style has been shown to be particularly useful for maneuver classification, with Phillips et. al @cite_31 using the Lankershim and Peachtree datasets to predict the turn a driver will take at a multi lane intersection based on surrounding vehicles, lateral lane position, which lane a vehicle is in, and legal turns that can be made from said lane. A similar technique can also be applied to smaller single lane intersection without signals based on vehicle location and speed @cite_17 .
@cite_31 @cite_17
2741087535 2741066535
Effective navigation of urban environments is a primary challenge remaining in the development of autonomous vehicles. Intersections come in many shapes and forms, making it difficult to find features and models that generalize across intersection types. New and traditional features are used to train several intersection intention models on real-world intersection data, and a new class of recurrent neural networks, Long Short Term Memory networks (LSTMs), are shown to outperform the state of the art. The models predict whether a driver will turn left, turn right, or continue straight up to 150 m with consistent accuracy before reaching the intersection. The results show promise for further use of LSTMs, with the mean cross validated prediction accuracy averaging over 85 for both three and four-way intersections, obtaining 83 for the highest throughput intersection. Advanced Driver Assistance Systems have been shown to greatly improve road safety. However, existing systems are typically reactive with an inability to understand complex traffic scenarios. We present a method to predict driver intention as the vehicle enters an intersection using a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). The model is learnt using the position, heading and velocity fused from GPS, IMU and odometry data collected by the ego-vehicle. In this paper we focus on determining the earliest possible moment in which we can classify the driver's intention at an intersection. We consider the outcome of this work an essential component for all levels of road vehicle automation.