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.
A commonly used dataset for this work is the NGSIM highway datasets, US-101 and I-80. This dataset was collected in 2005 using multiple overhead cameras observing sections of highway. This data was taken over three sections of 15 minutes each and contains trajectories of roughly 5000 vehicles. Visual tracking techniques were used to extract vehicles trajectories from the image data at a rate of 10Hz. Given the nature of tracking vehicles from a distant camera, the data suffers from considerable tracking noise @cite_24 , and even with aggressive filtering and correction techniques, significant problems still exist such as multiple collisions @cite_7 that do not exist in the real-world sample. We overcome these limitations with a modern data collection vehicle, as later described in Section .
@cite_24 @cite_7
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The Next Generation Simulation (NGSIM) trajectory data sets provide longitudinal and lateral positional information for all vehicles in certain spatiotemporal regions. Velocity and acceleration information cannot be extracted directly because the noise in the NGSIM positional information is greatly increased by the necessary numerical differentiations. A smoothing algorithm is proposed for positions, velocities, and accelerations that can also be applied near the boundaries. The smoothing time interval is estimated on the basis of velocity time series and the variance of the processed acceleration time series. The velocity information obtained in this way is then applied to calculate the density function of the two-dimensional distribution of velocity and inverse distance and the density of the distribution corresponding to the "microscopic" fundamental diagram. It is also used to calculate the distributions of time gaps and times to collision, conditioned to several ranges of velocities and velocity diff... A clear understanding of car following behavior and microscopic relationships is critical for advancing traffic flow theory. Without empirical microscopic data, plausible but incorrect hypotheses perpetuate in the vacuum. The Next Generation Simulation (NGSIM) project was undertaken to collect such data and the NGSIM data set has become the de facto standard, underlying the vast majority of empirically based advances of the past decade. But there has been a growing minority of researchers who have found unrealistic relationships in the NGSIM data. To date, the critical findings have almost exclusively come from the existing NGSIM database itself. Unfortunately, as this paper shows, the NGSIM errors are beyond anything that could be corrected strictly through cleaning or interpolation of the reported NGSIM data.