In order to drive safely in a dynamic environment, autonomous vehicles should be able to predict the future states of traffic participants nearby, especially surrounding vehicles, similar to the capability of predictive driving of human drivers. That is why researchers are devoted to the field of trajectory prediction and propose different methods. This paper is to provide a comprehensive and comparative review of trajectory-prediction methods proposed over the last two decades for autonomous driving. It starts with the problem formulation and algorithm classification. Then, the popular methods based on physics, classic machine learning, deep learning, and reinforcement learning are elaborately introduced and analyzed. Finally, this paper evaluates the performance of each kind of method and outlines potential research directions to guide readers. 

A Survey on Trajectory-Prediction Methods for Autonomous Driving

In order to drive safely in a dynamic environment, autonomous vehicles should be able to predict the future states of traffic participants nearby, especially surrounding vehicles, similar to the capability of predictive driving of human drivers. That is why researchers are devoted to the field of trajectory prediction and propose different methods. This paper is to provide a comprehensive and comparative review of trajectory-prediction methods proposed over the last two decades for autonomous driving. It starts with the problem formulation and algorithm classification. Then, the popular methods based on physics, classic machine learning, deep learning, and reinforcement learning are elaborately introduced and analyzed. Finally, this paper evaluates the performance of each kind of method and outlines potential research directions to guide readers.

Accurate monitoring the surrounding environment is an important research direction in the field of unmanned systems such as bio-robotics, and has attracted much research attention in recent years. The trajectories of surrounding vehicles should be predicted accurately in space and time to realize active defense and running safety of an unmanned system. However, there is uncertainty and uncontrollability in the process of trajectory prediction of surrounding obstacles. In this study, we propose a trajectory prediction method based on a sequential model, that fuses two neural networks of a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, a box plot is used to detect and eliminate abnormal values of vehicle trajectories, and valid trajectory data are obtained. Second, the trajectories of surrounding vehicles are predicted by merging the characteristics of CNN space expansion and LSTM time expansion; the hyper-parameters of the model are optimized according to a grid search algorithm, which satisfies the double-precision prediction requirement in space and time. Finally, data from next generation simulation (NGSIM) and Creteil roundabout in France are taken as test cases; the correctness and rationality of the method are verified by prediction error indicators. Experimental results demonstrate that the proposed CNN-LSTM method is more accurate and features a shorter time cost, which meets the prediction requirements and provides an effective method for the safe operation of unmanned systems.

Motion trajectory prediction based on a CNN-LSTM sequential model

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

Recently, mobility has gathered tremendous interest as the users’ desire for consecutive connections and better quality of service has increased. An accurate prediction of user mobility in mobile networks provides efficient resource and handover management, which can avoid unacceptable degradation of the perceived quality. Therefore, mobility prediction in wireless networks is of great importance and many works have been dedicated to this issue. In this paper, the necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed. Moreover, the learning perspective of solutions to mobility prediction has been studied. Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds of knowledge. At last, this paper also explores the open research challenges due to the advent of the fifth-generation mobile system and puts forward some potential trends in the near future.

/pdf/mobility-prediction-a-survey-on-state-of-the-art-schemes-and-1fqg2zsn96.pdf

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

Trajectory prediction of objects in moving objects databases (MODs) has garnered wide support in a variety of applications and is gradually becoming an active research area. The existing trajectory prediction algorithms focus on discovering frequent moving patterns or simulating the mobility of objects via mathematical models. While these models are useful in certain applications, they fall short in describing the position and behavior of moving objects in a network-constraint environment. Aiming to solve this problem, a hidden Markov model (HMM)-based trajectory prediction algorithm is proposed, called Hidden Markov model-based Trajectory Prediction (HMTP). By analyzing the disadvantages of HMTP, a self-adaptive parameter selection algorithm called HMTP $\ast$ is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed. In addition, a density-based trajectory partition algorithm is introduced, which helps improve the efficiency of prediction. In order to evaluate the effectiveness and efficiency of the proposed algorithms, extensive experiments were conducted, and the experimental results demonstrate that the effect of critical parameters on the prediction accuracy in the proposed paradigm, with regard to HMTP $\ast$ , can greatly improve the accuracy when compared with HMTP, when subjected to randomly changing speeds. Moreover, it has higher positioning precision than HMTP due to its capability of self-adjustment.

A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

GPS data often have position deviations in precision and are apt to be affected by noise; hence, it is essential to extract features from trajectories before performing large-scale data mining. A GeoHash-based spatial coding technique called GeoHashTree was used to index spatiotemporal trajectory points in order to improve the efficiency of nearest-neighbor search. The GeoHashTree was applied in trajectory clustering and an improved density-based clustering algorithm was proposed to reduce the time complexity of nearest-neighbor search from $O(n^2)$ to $O(n\\log{n})$. After extracting trajectory points with changing angles, the proposed clustering approach was employed to achieve deep-level feature extraction on trajectory points with changing angles, which aims to accurately identify feature points. Extensive experiments are conducted on real GPS data and the results demonstrate that the proposed trajectory-clustering algorithm based on the GeoHashTree spatial index structure can improve time performance by an average of 90.89% as well as guarantee the accuracy of clustering compared with the traditional clustering method. The visualization results show that the trajectory feature extraction approach can effectively find trajectory points with changing angles and discover a varying types of feature points from large-scale data sets. In addition, the proposed approach does not depend on road network data and can dynamically update with new incoming trajectory data as road networks change in real time.

/pdf/a-trajectory-feature-extraction-approach-based-on-spatial-io4c18xp8s.pdf

A trajectory feature extraction approach based on spatial coding technique

This paper proposed a path planning algorithm based on ray tracking and diffuseness, named Ray Tracking & Illumination Algorithm, which searches for paths by simulating natural ray propagation and diffuse reflection, and uses redundant node removal and Bezier curve smoothing to improve the quality of the path. At the same time, in order to comprehensively evaluate the safety, length, smoothness and other indicators of a path, this paper puts forward the concept of “Motion Delay”, and evaluates the quality of the algorithm through the statistical motion delay of the path generated from many runs. Finally, a comparative test was conducted on the MATLAB platform with the A*, RRT, Informed RRT* and ABC-PSO algorithms. The results show that the generated path of RTI is smoother and safer. Especially, RTI is faster, more robust in the environment with U-shaped trap and less sensitive to the size of the map.

Robotic Path Planning Algorithm based on Ray Tracking and Diffuseness

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A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

A trajectory feature extraction approach based on spatial coding technique

Robotic Path Planning Algorithm based on Ray Tracking and Diffuseness