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Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions

Vibha Bharilya, Neetesh Kumar

arXiv:2307.07527v1cs.LGcs.AI

TL;DR

Dedicated AV trajectory-prediction reviews of machine-learning methods, particularly newer vision and reinforcement-learning approaches, remain relatively unexplored. This survey synthesizes conventional, deep-learning, and reinforcement-learning work, and discusses their trade-offs, datasets, metrics, and open challenges.

  • Problem

    A comprehensive review dedicated to AV trajectory prediction using machine learning remains relatively unexplored, including coverage of newer vision-based and reinforcement-learning methods.

    Source: A. Motivation · p. 2

  • Method

    The survey organizes conventional, deep-learning, and reinforcement-learning approaches and reviews their strengths, weaknesses, datasets, and evaluation metrics.

    Source: C. Key Contributions · pp. 3–4

  • Results

    Deep-learning methods offer the highest prediction accuracy but are black-box, computationally costly, and limited to scenarios represented in training data.

    Source: §VII · pp. 26, 29

  • Takeaways & Limitations

    The survey identifies challenges including interdependent multi-agent trajectories and heterogeneous environments as areas for further research.

    Source: C. Key Contributions · p. 4, A. Challenges · p. 30

Abstract

from arXiv · show

Autonomous Vehicles (AVs) have emerged as a promising solution by replacing human drivers with advanced computer-aided decision-making systems. However, for AVs to effectively navigate the road, they must possess the capability to predict the future behavior of nearby traffic participants, similar to the predictive driving abilities of human drivers. Building upon existing literature is crucial to advance the field and develop a comprehensive understanding of trajectory prediction methods in the context of automated driving. To address this need, we have undertaken a comprehensive review that focuses on trajectory prediction methods for AVs, with a particular emphasis on machine learning techniques including deep learning and reinforcement learning-based approaches. We have extensively examined over two hundred studies related to trajectory prediction in the context of AVs. The paper begins with an introduction to the general problem of predicting vehicle trajectories and provides an overview of the key concepts and terminology used throughout. After providing a brief overview of conventional methods, this review conducts a comprehensive evaluation of several deep learning-based techniques. Each method is summarized briefly, accompanied by a detailed analysis of its strengths and weaknesses. The discussion further extends to reinforcement learning-based methods. This article also examines the various datasets and evaluation metrics that are commonly used in trajectory prediction tasks. Encouraging an unbiased and objective discussion, we compare two major learning processes, considering specific functional features. By identifying challenges in the existing literature and outlining potential research directions, this review significantly contributes to the advancement of knowledge in the domain of AV trajectory prediction.

I. INTRODUCTION

The introduction frames nearby-traffic trajectory prediction as important to AV safety and identifies a gap in dedicated reviews of machine-learning methods. It presents the survey’s scope and summarizes rising search interest in related topics.

I. INTRODUCTION

  • Because AV safety is not fully established, forecasting nearby traffic states supports planning decisions and anticipation of potential dangers.Prediction is difficult amid varied maneuvers, complex interactions, uncertain sensory information, and AV computation-time constraints.Source: §I · p. 1

A. Motivation

  • Motion prediction spans behavior, maneuvers, and detailed position-by-time trajectories, while a dedicated AV trajectory review of machine learning, including newer vision and reinforcement-learning work, remained underexplored.Source: A. Motivation · p. 2

B. Google Trends

C. Key Contributions

  • The survey organizes conventional, deep-learning, and reinforcement-learning methods, assesses their strengths and weaknesses, reviews datasets and metrics, and identifies challenges and research directions.Source: C. Key Contributions · pp. 3–4

D. Paper Organization

The paper organizes its review around trajectory prediction’s problem formulation and terminology, then surveys methods, evaluation practices, challenges, and research directions. It frames prediction as estimating target vehicles’ future states from available information and defines the terms used to describe vehicles and predictions.

D. Paper Organization

  • Sections III–V review conventional, deep-learning, and reinforcement-learning methods; later sections cover datasets and metrics, performance, challenges, future directions, and conclusions.Source: D. Paper Organization · p. 4

II. PROBLEM DESCRIPTION

  • Trajectory prediction is framed as estimating target vehicles’ future-state sequences probabilistically, conditioned on information available to the ego vehicle.Available information combines historical states and optional environmental information.Source: §II · p. 4
  • Historical inputs cover traffic participants through time t-1 and may include position, velocity, acceleration, and orientation; each target vehicle can be predicted independently.The formulation also identifies road context and monitored surrounding and target vehicles as relevant to accurate prediction.Source: §II · p. 4

A. Terminolgy

  • Target vehicles are the vehicles being forecast, surrounding vehicles may affect their behavior, and non-effective vehicles are treated as having no bearing on it.The terminology also distinguishes single-trajectory unimodal predictions from multiple-trajectory multimodal predictions.Source: A. Terminolgy · p. 4

B. Techniques applied for Trajectory-Prediction Methods

The survey groups trajectory-prediction methods into conventional, deep-learning, and reinforcement-learning approaches. It describes conventional techniques and several neural-network approaches, including recurrent, convolutional, and generative models.

B. Techniques applied for Trajectory-Prediction Methods

1) Conventional Methods

  • Conventional methods use physics, kinematics, filters, Markov models, and probabilistic models; they are often efficient and easy to implement but can struggle with complex interactions and uncertainty.Source: 1) Conventional Methods · p. 5

2) Deep learning-based methods

5) Transformer

The section surveys transformer and reinforcement-learning approaches alongside conventional trajectory-prediction methods. It describes the conventional classes, their strengths and limitations, and the scope of deep-learning and RL methods.

5) Transformer

  • Transformer models use attention to capture long-range dependencies and interactions between agents when generating trajectory predictions.The section also notes that deep-learning methods can require substantial labeled data and computation, and can be difficult to interpret.Source: 5) Transformer · p. 6
  • Reinforcement-learning approaches include inverse RL, which infers reward functions from expert demonstrations, deep IRL, and imitation learning –.The survey flags trade-offs involving algorithm complexity, training-data availability, generalization across driving scenarios, and real-world uncertainty.Source: 5) Transformer · pp. 6–7

III. CONVENTIONAL TRAJECTORY PREDICTION METHOD

  • Conventional methods are classified as physics-based, sampling, and probabilistic; the survey reports 56% probabilistic, 35% sampling, and 9% sampling papers.The passage repeats “sampling” for the remaining 9%, so the third reported share is not assigned to another class here.Source: §III · p. 7

A. Physics based Models

  • Physics-based models use dynamic or kinematic motion equations and are explainable and robust, especially for predictions within a horizon of no more than 1 second.They may struggle with complex maneuvers and dynamic or unknown environments because of simplified assumptions and limited adaptability.Source: A. Physics based Models · p. 7

B. Sampling Methods

  • Sampling methods generate distributions of possible future states, but can be computationally complex, miss important trajectory regions, and require efficient sampling strategies.Examples combine maneuver or interaction modeling with Monte Carlo simulation to generate candidate trajectories.Source: B. Sampling Methods · pp. 7–8

C. Probabilistic Models

The section reviews probabilistic trajectory models that represent uncertainty, then describes how GMM, GP, HMM, and DBN approaches model trajectory patterns, temporal structure, and interactions. It also contrasts these methods with deep learning’s use of physical, road, and interaction-related factors.

C. Probabilistic Models

  • Probabilistic trajectory prediction estimates distributions of future trajectories conditioned on past trajectories, sensor measurements, and other relevant information.These frameworks represent uncertainty and variability in predictions.Source: C. Probabilistic Models · p. 8

A Gaussian Mixture Model

  • Gaussian Mixture Models represent trajectory distributions as multiple Gaussian modes, allowing multimodal behavior patterns but requiring computation and selection of the number of components.GMM-HMM and related approaches are also used to model interactions and maneuvers.Source: A Gaussian Mixture Model · p. 8, C. Probabilistic Models · p. 8

2) Gaussian Process

  • Gaussian Processes fit mean and covariance parameters to observed trajectory samples to produce probabilistic forecasts, but sample-based approaches may transfer poorly to new contexts.The section describes combining GPs with HMMs, Dirichlet processes, and prototype trajectories.Source: 2) Gaussian Process · pp. 8–9

3) Hidden Markov Model

  • HMMs estimate likely future observation sequences from past states and can handle temporal dependencies and noisy or missing data, while assuming hidden states are Markovian.Under this assumption, transition probabilities depend only on the current state.Source: 3) Hidden Markov Model · p. 9

4) Dynamic Bayesian Network

  • Dynamic Bayesian Networks incorporate time sequences and vehicle interactions, but methods may distinguish only two or three maneuvers, limiting generalization across scenarios.The section describes time templates as discretizations of continuous time into points with predetermined granularity.Source: 4) Dynamic Bayesian Network · p. 9

IV. DEEP LEARNING-BASED PREDICTION METHODS

  • Deep-learning trajectory methods consider vehicle properties, road features, and interactions, whereas conventional techniques are described as effective mainly in basic, short-term prediction settings.The section introduces an overview of prevalent deep-learning methods.Source: §IV · pp. 9–10, Figure 7 · p. 10

A. Sequential Modelling

Sequential trajectory prediction methods encode historical trajectories using RNNs, TCNs, attention, or transformers; the survey notes TCNs are less commonly used than the other listed algorithms. It details RNN architectures and TCN capabilities and limitations.

A. Sequential Modelling

  • TCNs are less commonly used in AV trajectory prediction than RNNs, attention mechanisms, and transformers.The survey’s sequential-modeling overview presents the distribution of research papers by algorithm in Fig. 8.Source: A. Sequential Modelling · p. 10, Figure 8 · p. 10

1) Recurrent Neural Network

  • Single RNNs support maneuver-based or single-modal prediction, while multiple RNN architectures combine encoders, decoders, and interaction modeling for multimodal trajectory prediction.The reviewed approaches include separate LSTMs for lane and trajectory prediction and maneuver-specific LSTM decoders.Source: 1) Recurrent Neural Network · p. 10
  • RNNs model temporal dependencies and variable-length sequences, but can suffer vanishing or exploding gradients and sequential-computation limitations.Gated LSTMs and GRUs were developed to address gradient problems in recurrent networks.Source: 1) Recurrent Neural Network · pp. 10, 13

2) Temporal convolutional networks

  • TCNs process historical trajectories and can handle variable-length sequences without information leakage, with efficient parallel computation and receptive fields that can capture short- and long-term dynamics.Spatial relationships and long-term memory may require additional considerations.Source: 2) Temporal convolutional networks · p. 13

3) Attention Mechanism

Attention methods focus on relevant spatial and temporal information to model vehicle interactions, and transformer variants extend this to complex dependencies across agents and scenes. The vision-based discussion contrasts bird’s-eye and ego-camera views, noting practical advantages and constraints of each.

3) Attention Mechanism

  • Attention models combine spatial and temporal information to represent nearby-vehicle interactions and selectively focus on relevant context for trajectory prediction.The survey also identifies computational overhead, model complexity, attention bias, and data dependence as potential drawbacks.Source: 3) Attention Mechanism · p. 13
  • Transformer approaches represent spatiotemporal interactions, temporal sequences, agent relationships, and road-graph context, but require substantial computation and may face interpretability and data-efficiency challenges.S2TNet uses spatiotemporal Transformers for interactions and temporal Transformers for sequences [111].Source: 3) Attention Mechanism · p. 15

B. Vision Based Modelling

  • Bird’s-eye-view prediction offers a broader field of view and more accurate predictions, while ego-camera prediction has a narrower view, limited horizon, and greater susceptibility to obstructions.The survey notes ego-camera prediction may be more useful where vehicles lack cameras that locate target agents and road BEVs.Source: B. Vision Based Modelling · p. 15
  • Ego-camera prediction uses the ego vehicle’s perspective and visual cues such as bounding boxes, RGB frames, position, speed, and vehicle type to predict future locations.Fig. 10 depicts the participation of vision-based techniques in trajectory-prediction research.Source: Figure 9 · p. 15, Figure 10 · p. 15, B. Vision Based Modelling · p. 15

1) Convolutional Neural Network

CNN-based methods use trajectory, image, or bird’s-eye-view representations to capture spatial patterns, while graph neural networks represent interacting traffic participants and maps as graphs. The survey describes their architectures and trade-offs, including CNNs’ temporal modeling challenges and GNNs’ graph-design and scalability concerns.

1) Convolutional Neural Network

  • Nikhil et al. found CNN trajectory prediction superior to RNN and faster in their experiments, using convolutional and fully connected layers to produce future trajectories from past ones.The cited approach places a convolutional layer after a fully connected layer to model temporal continuity.Source: 1) Convolutional Neural Network · p. 17
  • CNN methods commonly use bird’s-eye-view images built from sources such as LiDAR, occupancy grids, or HD maps, and can output candidate trajectories and their likelihoods.Other approaches use context rasterization, semantic properties, heatmaps, or point clouds; the survey notes computational cost and information loss when processing raster maps.Source: 1) Convolutional Neural Network · p. 17

2) Graph Neural Network

  • GCNs model interaction-aware features from graph nodes and neighbors; surveyed designs combine fixed or dynamic graphs with temporal modules such as LSTMs, CNNs, and GRUs.The survey identifies spatial-dependency modeling and irregular graph handling as strengths, while flagging scalability, graph construction, and temporal modeling as considerations.Source: 2) Graph Neural Network · pp. 17–18
  • GATs use attention instead of statically normalized convolution to weight relevant nodes, with surveyed models combining graph attention with recurrent or Transformer components.Their performance depends heavily on graph quality and representation, including appropriate node and edge selection.Source: 2) Graph Neural Network · pp. 18–19
  • Graph methods also represent vector-map elements and vehicles as nodes, enabling map and interaction information to inform trajectory prediction.Examples include VectorNet, CNN/GCN feature integration, target-driven goal and route selection, and LaneRCNN’s local lane maps and interaction modules.Source: 2) Graph Neural Network · p. 19

C. Combination of CNNs and RNNs

The survey describes hybrid CNN–RNN models that combine spatial and temporal information, alongside generative models for multimodal trajectory prediction. GANs generate trajectories through generator–discriminator training, while CVAEs condition generation on contextual inputs whose effectiveness affects performance.

C. Combination of CNNs and RNNs

  • CNN–RNN hybrids combine spatial and temporal processing, for example by feeding LSTM-encoded vehicle histories and social interactions into CNNs or fusing convolutional scene features with recurrent dynamics.Other surveyed designs use CNN-LSTM networks to extract features from the primary vehicle’s state and nearby objects.Source: C. Combination of CNNs and RNNs · pp. 19–20

D. Generative Model

  • Generative models target diverse, socially acceptable, and controllable multimodal futures, a difficult objective when training relies on only one ground truth.The survey reports approximately equal participation by GANs and VAEs in trajectory prediction.Source: D. Generative Model · p. 20
  • GANs generate a future trajectory from historical data and train a discriminator to assess realism, with surveyed variants adding social mechanisms, maps, interaction information, or temporal modeling.The survey also describes GAN-based short-term prediction and approaches incorporating Signal Temporal Logic rules.Source: D. Generative Model · p. 21
  • CVAEs generate trajectories conditioned on vehicle state, surrounding traffic, road conditions, or other context, and their performance depends heavily on the chosen conditioning inputs.Surveyed applications include filtering irrational scenarios with Signal Temporal Logic and predicting multimodal trajectories for all scene participants.Source: D. Generative Model · p. 22

V. REINFORCEMENT LEARNING-BASED METHODS

The survey presents reinforcement learning for trajectory prediction as reward- or policy-based learning, commonly framed as an MDP that maximizes cumulative reward. It describes IRL as inferring rewards from demonstrations and notes ambiguity, demonstration-quality, and computational challenges.

V. REINFORCEMENT LEARNING-BASED METHODS

  • RL trajectory prediction commonly uses an MDP to maximize projected cumulative reward, and methods may estimate a cost function or directly identify an optimal policy.The survey groups these approaches into IRL, imitation learning, and deep IRL.Source: §V · p. 23

A. Inverse Reinforcement Learning

  • IRL infers a reward function from expert demonstrations to explain observed behavior and derive a driving policy, rather than directly imitating the demonstrated trajectories.Surveyed applications include spatiotemporal state lattices and probabilistic predictions that account for interactions.Source: A. Inverse Reinforcement Learning · p. 23
  • An optimal policy may correspond to multiple reward functions; MaxEntIRL addresses this ambiguity by maximizing entropy across potential state-action-pair distributions.The survey cautions that IRL depends on expert demonstrations and their quality, and presents computational complexity as a practical concern.Source: A. Inverse Reinforcement Learning · p. 23

B. Deep Inverse Reinforcement Learning

The survey describes Deep IRL as using deep neural networks to learn reward functions from expert demonstrations for trajectory prediction. It highlights reward-modeling applications and notes challenges involving data, computation, interpretability, and overfitting.

B. Deep Inverse Reinforcement Learning

  • One surveyed approach produces a distribution of diverse future states and actions, with trajectory generation intended to scale to large road networks and describe route distributions in synthetic-data generation.Source: B. Deep Inverse Reinforcement Learning · p. 24
  • A surveyed approach does not account for traffic conditions or interactions with other vehicles.Source: B. Deep Inverse Reinforcement Learning · p. 24
  • Deep IRL uses deep neural networks to learn reward functions from expert demonstrations, with surveyed approaches drawing on camera, LiDAR, trajectory, and road-geometry information.Examples include convolutional reward approximation, ConvNets and state-visiting frequencies, and LSTMs that extract LiDAR and trajectory features.Source: B. Deep Inverse Reinforcement Learning · p. 25
  • A goal- and pathway-specific reward update can reduce the need for preset goal states compared with using an original complex reward.The surveyed method learns state rewards for goals and pathways individually.Source: B. Deep Inverse Reinforcement Learning · p. 25
  • Deep IRL applications face challenges involving data requirements, computational complexity, interpretability, and overfitting.Source: B. Deep Inverse Reinforcement Learning · p. 25

C. Imitation Learning

The survey describes imitation learning, including GAIL, as an approach to learning driving policies from expert behavior when reward signals are limited. It also reviews trajectory-prediction datasets and evaluation metrics.

C. Imitation Learning

  • Imitation learning addresses reward-sparse RL by learning from expert observations; GAIL trains a generator to mimic expert trajectories and a discriminator to distinguish them.The survey also describes behavior cloning as straightforward but data-intensive and vulnerable to unknown states.Source: C. Imitation Learning · p. 25

VI. TRAINING AND EVALUATION

  • Modern datasets broadened prediction research beyond highway-only settings with more annotated frames, multiple sensors, HD maps, diverse conditions, and more agent classes, but still lack intention labels.NGSIM-180 and highD focus on highway cars; newer datasets include varied cities, weather, ego odometry, and road-rule labels.Source: §VI · p. 26
  • ADE and FDE measure mean displacement over the trajectory and at its final horizon, while Best of N and NLL assess multimodal predictions.The survey also identifies WSADE and WSFDE as metrics frequently used on the ApolloScape dataset.Source: §VI · p. 26

VII. DISCUSSION

The survey compares deep-learning and reinforcement-learning approaches, describing their capabilities alongside tradeoffs in interpretability, data needs, computation, and prediction output. It also outlines challenges that make AV trajectory prediction difficult.

VII. DISCUSSION

  • Deep-learning models have the highest prediction accuracy in the current state of the art and predict up to 8 seconds, but remain data-dependent, computationally costly, and hard to explain.Their holistic inputs can include agent interactions and semantic data, while adaptability is limited to scenarios represented in training data.Source: §VII · pp. 26, 29
  • Reinforcement-learning models can represent complex maneuvers and long-term predictions, but often output discrete actions and depend on expert observations to learn robust policies.The survey notes that holistic RL models can be computationally costly and that policy-learning complexity can reduce prediction accuracy.Source: §VII · p. 29

A. Challenges

  • Prediction is challenged by uncertainty, nonlinear dynamics, limited sensor coverage and data, long horizons, complex roads, and multimodal futures.Occlusion and weather can hinder tracking, while small errors can compound over prediction horizons of at least three seconds.Source: A. Challenges · p. 29, Figure 18 · p. 30
  • Sparse or noisy observations, interdependent agents, heterogeneous road users, safety-critical use, and real-time constraints further complicate trajectory prediction.Models must account for agent types and context while generating reliable predictions efficiently enough for real-time operation.Source: A. Challenges · p. 30

B. Future Research Directions

The survey identifies research directions spanning contextual and intention-aware prediction, sensor integration, uncertainty, human behavior, real-time operation, safety, and communication. It also recommends exploring hybrid approaches and emphasizes robust, interpretable models and new sensor modalities.

B. Future Research Directions

  • Future work could incorporate road context, traffic rules, and driver intention, fuse multiple sensor modalities, model uncertainty, and predict pedestrians’ and cyclists’ behavior.Suggested approaches include probabilistic models such as Bayesian neural networks and improved prediction in crowded urban environments.Source: B. Future Research Directions · pp. 30–31

5) Real-time implementation & Hardware acceleration:

  • Real-time prediction research could optimize algorithms and develop hardware architectures for efficient computation.The survey presents real-time processing as a requirement for AV trajectory prediction.Source: 5) Real-time implementation & Hardware… · p. 31

B. Future Research Directions

  • Safety and robustness directions include handling rare events, improving predictions in adverse weather, and considering ethical aspects of prediction algorithms.The survey names heavy rain, snow, fog, and low visibility as challenging conditions.Source: 5) Real-time implementation & Hardware… · p. 31
  • Other directions include predicting motion relative to the ego vehicle, accounting for random obstacles, and sharing real-time information through V2V and V2X communication.The suggested shared information includes position, speed, acceleration, and intentions.Source: 5) Real-time implementation & Hardware… · p. 31

11) Hybridization of several approaches:

  • Hybridizing trajectory-prediction approaches is proposed as a direction that could support more accurate and robust predictions depending on context and requirements.The survey notes that hybridization can take different forms.Source: 11) Hybridization of several approaches · p. 31

IX. CONCLUSION

  • The survey concludes that recent advances show promise while identifying a continuing need for more robust, interpretable models and exploration of new sensor modalities.It presents the review as a reference for researchers and practitioners.Source: §IX · p. 31

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