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.
Path prediction models attempt to produce a future trajectory of the vehicle given some data on the vehicle's past. Often, physical based kinematic or dynamic models are used in conjunction with Switching Kalman Filters @cite_16 , Monte Carlo Simulations @cite_29 , or Variational Gaussian Mixture Models @cite_30 . These methods will output a single prediction proposal, and give a measure of uncertainty. A maneuver recognition system can be combined with a Gaussian Process based path predictor to allow multi-modal prediction @cite_27 .
@cite_30 @cite_27 @cite_29 @cite_16
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In the context of driver assistance, an accurate and reliable prediction of the vehicle's trajectory is beneficial. This can be useful either to increase the flexibility of comfort systems or, in the more interesting case, to detect potentially dangerous situations as early as possible. In this contribution, a novel approach for trajectory prediction is proposed which has the capability to predict the vehicle's trajectory several seconds in advance, the so called long-term prediction. To achieve this, previously observed motion patterns are used to infer a joint probability distribution as motion model. Using this distribution, a trajectory can be predicted by calculating the probability for the future motion, conditioned on the current observed history motion pattern. The advantage of the probabilistic modeling is that the result is not only a prediction, but rather a whole distribution over the future trajectories and a specific prediction can be made by the evaluation of the statistical properties, e.g. the mean of this conditioned distribution. Additionally, an evaluation of the variance can be used to examine the reliability of the prediction. Maneuver recognition and trajectory prediction of moving vehicles are two important and challenging tasks of advanced driver assistance systems (ADAS) at urban intersections. This paper presents a continuing work to handle these two problems in a consistent framework using non-parametric regression models. We provide a feature normalization scheme and present a strategy for constructing three-dimensional Gaussian process regression models from two-dimensional trajectory patterns. These models can capture spatio-temporal characteristics of traffic situations. Given a new, partially observed and unlabeled trajectory, the maneuver can be recognized online by comparing the likelihoods of the observation data for each individual regression model. Furthermore, we take advantage of our representation for trajectory prediction. Because predicting possible trajectories at urban intersection involves obvious multimodalities and non-linearities, we employ the Monte Carlo method to handle these difficulties. This approach allows the incremental prediction of possible trajectories in situations where unimodal estimators such as Kalman Filters would not work well. The proposed framework is evaluated experimentally in urban intersection scenarios using real-world data. The probabilistic prediction of road traffic scenarios is addressed. One result is a probabilistic occupancy of traffic participants, and the other result is the collision risk for autonomous vehicles when executing a planned maneuver. The probabilistic occupancy of surrounding traffic participants helps to plan the maneuver of an autonomous vehicle, whereas the computed collision risk helps to decide if a planned maneuver should be executed. Two methods for the probabilistic prediction are presented and compared: 1) Markov chain abstraction and 2) Monte Carlo simulation. The performance of both methods is evaluated with respect to the prediction of the probabilistic occupancy and the collision risk. For each comparison test, we use the same models that generate the probabilistic behavior of traffic participants, where the generation of these data is not compared with real-world data. However, the results independently show the behavior generation that Markov chains are preferred for the probabilistic occupancy, whereas Monte Carlo simulation is clearly preferred for determining the collision risk.