Anticipating possible behaviors of traffic participants is an essential capability of autonomous vehicles. Many behavior detection and maneuver recognition methods only have a very limited prediction horizon that leaves inadequate time and space for planning. To avoid unsatisfactory reactive decisions, it is essential to count long-term future rewards in planning, which requires extending the prediction horizon. In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern. We adopt a recurrent neural network (RNN) for observation encoding, and based on that, we propose a novel vehicle behavior interaction network (VBIN) to capture the vehicle interaction from the hidden states and connection feature of each interaction pair. The output of our method is a probabilistic likelihood of multiple behavior classes, which matches the multimodal and uncertain nature of the distant future. A systematic comparison of our method against two state-of-the-art methods and another two baseline methods on a publicly available real highway dataset is provided, showing that our method has superior accuracy and advanced capability for interaction modeling.
Many methods focus on trajectory prediction and generate a time-profiled trajectory directly. Regression methods, such as linear regressions @cite_2 and non-linear Gaussian process regression models @cite_10 @cite_26 @cite_17 , are extensively studied. Recurrent neural networks (RNNs) have recently been widely applied to various trajectory prediction tasks including trajectory prediction for pedestrians and vehicles. For vehicles, apart from predicting trajectories, many methods also aim to predict high-level intentions @cite_1 @cite_3 @cite_11 @cite_27 . Moreover, some recent works suggest that trajectory prediction can be augmented by the prediction of high-level semantic behaviors, e.g., LCs and insertions, due to the multimodal nature of the problem @cite_0 @cite_6 @cite_21 . In this paper, we focus on the behavior prediction problem.
@cite_26 @cite_11 @cite_21 @cite_1 @cite_3 @cite_6 @cite_0 @cite_27 @cite_2 @cite_10 @cite_17
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Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets. We also analyze the trajectories predicted by our model to demonstrate the motion behaviour learned by our model. We present an interpretable framework for path prediction that learns scene-specific causations behind agents' behaviors. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-down view of the scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns "where to look" in the large image when solving the path prediction task. While previous works on trajectory prediction are constrained to either use semantic information or hand-crafted regions centered around the agent, our method has the capacity to select any region within the image, e.g., a far-away curve when predicting the change of speed of vehicles. To study our goal towards learning observable causality behind agents' behaviors, we have built a new dataset made of top view images of hundreds of scenes (e.g., F1 racing circuits) where the vehicles are governed by known specific regions within the images (e.g., upcoming curves). Our algorithm successfully selects these regions, learns navigation patterns that generalize to unseen maps, outperforms previous works in terms of prediction accuracy on publicly available datasets, and provides human-interpretable static scene-specific dependencies. We present an interpretable framework for path prediction that leverages dependencies between agents’ behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents’ trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents’ behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net’s ability to generalize to unseen scenes. In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model. The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles. The proposed scheme feeds the sequence of vehicles' coordinates obtained from sensor measurements to the LSTM and produces the probabilistic information on the future location of the vehicles over occupancy grid map. The experiments conducted using the data collected from highway driving show that the proposed method can produce reasonably good estimate of future trajectory.