# Single-Vehicle Trajectory Prediction: A Review and Experimental Embedded Assessment

Oumaima Skalli, Sergio Rodriguez Florez, Abdelhafid El Ouardi, Stefano Masi

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Oumaima Skalli, Sergio Rodriguez Florez, Abdelhafid El Ouardi, Stefano Masi. Single-Vehicle Trajectory Prediction: A Review and Experimental Embedded Assessment. IEEE Open Journal of Vehicular Technology, 2026, 7, pp.639 - 658. 10.1109/OJVT.2026.3658986. hal-05484963

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Received 5 January 2026; accepted 22 January 2026. Date of publication 28 January 2026;
date of current version 13 February 2026. The review of this article was coordinated by Editor Giambattista Gruosso.

Digital Object Identifier 10.1109/OJVT.2026.3658986

# Single-Vehicle Trajectory Prediction: A Review and Experimental Embedded Assessment

OUMAIMA SKALLI ◦<fnref n="1" />,<fnref n="2" />, SERGIO RODRIGUEZ FLOREZ ◦<fnref n="1" />, ABDELHAFID EL OUARDI ◦<fnref n="1" />, AND STEFANO MASI ◦<fnref n="2" /> (Associate Member, IEEE)

CORRESPONDING AUTHOR: OUMAIMA SKALLI (e-mail: oumaima.skalli@universite-paris-saclay.fr).

This work was supported in part by Conventions Industrielles de Formation par la Recherche (CIFER) PhD funded by Ampere Software Technology, in part by the French National Association for Research and Technology (ANRT), and in part by ANRT as part of a CIFRE PhD Program, established through a collaboration between Ampere Software Technology and SATIE Laboratory.

**ABSTRACT** Due to technological advances in the automotive field, advanced driver assistance systems have attracted increasing interest from various research and development entities. Predicting road users' future trajectories remains an active research challenge for advanced driver assistance systems. Accurate Trajectory Prediction (TP) allows anticipation of surrounding road users' future motion, enabling timely safety-critical interventions such as speed regulation and emergency braking in unexpected driving situations. Recent advances in TP methods based on artificial intelligence have demonstrated remarkably accurate results compared to traditional methods. However, many of these models require a high computational burden, which makes their deployment on embedded architectures with constrained resources challenging. To overcome these constraints, TP models need to be lightweight and efficient to meet the real-time and power consumption requirements of advanced driver assistance systems. In other words, they must maintain high accuracy while guaranteeing low computational load and rapid inference. This paper presents a comparative and experimental review of state-of-the-art vehicle TP models. First, we propose a new taxonomy based on the operating environment, the trajectory output type, and the employed modeling approach to classify existing methods. Then, we evaluate representative approaches w.r.t the taxonomy in terms of accuracy, model complexity, computational performance, and real-time feasibility across a high-performance architecture and an embedded architecture. Finally, we discuss the evaluation results and present key conclusions and future directions.

**INDEX TERMS** Advanced driver assistance systems, hardware–software trade-off analysis, real-time performance evaluation, single vehicle trajectory prediction.

## I. INTRODUCTION

According to the World Health Organization's Global Status Report on Road Safety, 2023 [1], the annual number of road traffic deaths in 2021 was estimated to be 1.19 million. In response, the United Nations Decade of Action for Road Safety 2021–2030 is working to reduce road deaths by half by 2030. It has been shown [2] that the integration of Advanced Driver Assistance Systems (ADAS) in vehicles is an effective approach towards reducing human errors and promoting safer roads. Masello et al. [3] analyzed the measurable effect of ADAS in mitigating accidents in 18 different driving scenarios. The study demonstrated a 23,8% reduction in road

accidents in the U.S. with the integration of six ADAS. In general, ADAS applications leverage information produced by a process that begins with acquiring raw sensor data (e.g., from cameras, radar, or LiDAR) and continues with preprocessing steps such as calibration, synchronization, and data structuring. After preprocessing, the refined data is passed to a Multi-Object Detection (MOD) module [4], [5], which processes sensor data to identify and categorize perceived objects. Then, the detection outputs are passed to a Multi-Object Tracking (MOT) module [6], [7], which assigns unique identifiers to each detected object and maintains their continuous identification over time. In addition, the MOT module

<footnote n="1">¹Université Paris-Saclay, ENS Paris-Saclay, CNRS, SATIE, 91190 Gif-sur-Yvette, France</footnote>

<footnote n="2">²Ampere Software Technology, RGE-AMS DT1, 78280 Guyancourt, France</footnote>
<figure><img src="image_2.png" /><figcaption>**FIGURE 1.** Overview examples of (a) modular decomposition solution and (b) end-to-end solution for ego/object TP used for ACC.</figcaption></figure>

can also estimate current unobserved states, such as velocity and acceleration, if they are not captured by sensors. Next, a trajectory prediction module forecasts the future motion of the tracked objects, either jointly with the ego vehicle or for only one of them. After that, information from the TP module and others are incorporated into ADAS applications, depending on their need. For instance, Adaptive Cruise Control (ACC) [8], an ADAS that adjusts the ego vehicle's speed to maintain a safe distance from the lead vehicle, can employ the ego vehicle's predicted trajectories to identify the appropriate lead vehicle. Furthermore, it can also use predicted trajectories of surrounding vehicles to identify cut-in and cut-out maneuvers, which could lead to a sudden change in the ACC lead vehicle. Woo et al. [9] proposed an advanced ACC system that adjusts the ego vehicle's speed to minimize collision risk with eight surrounding vehicles. This risk is estimated based on their predicted trajectories, and the proposed method reduced it by 35%. While the described modular decomposition—covering acquisition, preprocessing, detection, tracking, prediction, and ADAS applications—is generally adopted, decision-making and prediction can also be addressed using end-to-end approaches [10], where raw sensor data is mapped directly to prediction outputs or decisions, without explicit intermediate stages. Fig. 1 illustrates an example of (a) a modular decomposition approach and (b) an end-to-end approach applied to an ACC application.

Predicting road users' trajectories for ADAS deals with various challenges. First, meeting real-time specifications remains a critical issue [11]. This is because perception, environment interpretation, decision-making, and control must be executed at high frequency to ensure rapid responsiveness to sudden events. Furthermore, managing computational resources and memory consumption is also a challenging specification that can be addressed through Hardware/Software co-design to reach the appropriate balance between accuracy and computational performance [12]. Additionally, the diversity of maneuvers performed by the road agents and the complexity of their interactions [13] significantly increase the variety of possible future trajectories.

Several solutions have been explored in the literature to address these challenges, ranging from physics-based models [14] to learning-based models [15]. While

physics-based TP methods such as constant velocity [16], constant acceleration [17], or constant turn rate and acceleration [18] are interpretable and light to compute, they are often limited to specific driving scenarios. In contrast, learning-based methods [19] [20], including Recurrent Neural Networks (RNN)-based architectures, graphical neural networks, attention-based models, and convolutional social pooling, can capture agents' intentions and interactions, and the environmental context, which offers improved generalization. However, these methods are more demanding in terms of resources and processing time, often lack interpretability, and can produce kinematically unrealistic trajectories if they do not incorporate physical constraints or vehicle dynamics [21]. To overcome this, recent works have incorporated kinematic constraints into Deep Learning (DL) models by integrating kinematic layers, as shown in [22] and [23].

As for the challenges related to real-time deployment on embedded systems such as ADS, several studies have addressed the general problem of Artificial Intelligence (AI) model deployment. Zhang et al. [24] reviewed hardware acceleration platforms and model compression strategies for adapting neural networks to resource-constrained devices. Seng et al. [25] analyzed embedded AI techniques based on training and inference speed, energy and power consumption, memory footprint, and scalability. They also categorized optimization strategies into four levels: system, algorithm, architecture, and technology level. In the specific context of TP, recent works have focused on balancing model accuracy with deployment constraints such as latency and model complexity. VectorNet [26] introduced a vectorized graph-based TP method that reduced model parameters by 70% and decreased the number of floating operations compared to rasterized ConvNet models while maintaining accuracy. However, evaluations were not performed on embedded architectures, and inference latency was not reported. DeepTrack [27] directly tackled real-time deployment challenges during the design phase by using a lightweight model based on temporal convolutional networks and depthwise convolutions, reducing model parameters and the number of operations compared to LSTM-based baselines. However, its performance was only tested on a Tesla V100 HPC GPU, with no reported inference latency. In contrast, EMP [28] explicitly optimized both training and inference time and showed consistently lower latency than other State-Of-The-Art (SOTA) methods, but the evaluation was also limited to non-embedded GPUs (V100, RTX 2080 Ti, RTX 4090).

Despite these advances, existing studies often evaluate model complexity or inference time in isolation, overlooking the complete processing time [28] [29]. In particular, factors such as data preprocessing and memory transfers are rarely integrated into the computational assessment. Furthermore, evaluations are typically performed on high-performance GPUs, which do not accurately reflect the computational constraints of embedded systems in vehicles. This paper aims to bridge this gap by evaluating SOTA TP methods based on
prediction accuracy, model complexity, and full processing
time in high-performance and embedded architectures. The
contributions of this work are:

1) A new taxonomy of single-vehicle TP methods, based on a comprehensive review of existing SOTA taxonomies. The proposed taxonomy addresses the lack of environment-type considerations in existing classifications and distinguishes between different modeling representations used during prediction. Subsequently, SOTA approaches are analyzed within this taxonomy, highlighting the strengths and limitations of each class.

2) Evaluation of representative methods from each class on a high-performance GPU architecture, based on accuracy, model complexity, and end-to-end processing time (preprocessing, inference, and data transfer). Promising methods are then selected and evaluated on an embedded architecture using the same metrics to analyze their performance when deployed in an embedded architecture.

3) Analysis of the influence of hardware architectures on performance, leading to key conclusions and suggestions for future research directions.

The remainder of this article is organized as follows:
Section II defines the key terminologies and formalizes the
TP problem. Section III reviews existing taxonomies and
introduces the proposed taxonomy. Section IV analyzes ex-
isting approaches with respect to the taxonomy. Section V
introduces the evaluation methodology, detailing the choice
of dataset, metrics, and algorithms. Finally, Section VI syn-
thesizes the evaluation results for the selected approaches on
both the high-performance architecture and the embedded ar-
chitecture, and the last Section VII discusses future directions
and conclusions.

II. PROBLEM FORMULATION

To ensure consistent understanding throughout this paper, we
begin by clarifying some key definitions. In this work, the
term motion prediction refers to forecasting the future motion
of road users over time, including maneuvers, trajectories,
or behaviors [30]. A maneuver (or intention) is a specific
driving action, such as changing lanes. Behavior describes
the driver's style of executing that action, while a trajectory
is the sequence of position coordinates over time. This paper
focuses on trajectory prediction, which consists of estimating
the future positions of a road user over a prediction time
horizon.

Based on this definition, TP can be approached in two ways: multi-agent prediction, where the future trajectory of multiple heterogeneous road users are jointly predicted [31] [32]; and single-agent prediction, which focuses on forecasting for each scene, the trajectory of a single road user. This paper emphasizes SOTA methods for single-vehicle TP, applicable to either an ego vehicle or a surrounding vehicle. The term ego vehicle refers to the sensor-equipped vehicle, while the Vehicle of Interest (VoI) denotes the vehicle being predicted. The main difference between ego vehicle TP and surrounding vehicle

TP lies in the information available on the vehicle. The ego
vehicle accesses its internal state via proprioceptive sensors,
while surrounding vehicle states are extracted from its extero-
ceptive sensors. Accordingly, this work does not assume any
form of vehicle-to-vehicle (V2V) communication. Moreover,
we distinguish between unimodal and multimodal prediction
approaches. In the unimodal case, a single trajectory is pre-
dicted, while multimodal methods generate multiple plausible
future trajectories, each with an associated probability. This
paper considers both output types.

In single-vehicle TP, the VoI is first identified. Then, in some methods, prediction is based only on the most recent state of the VoI, such as $S_{Vol} = \{s_t\}$. For other methods, the past states are collected over an observation window of length $T_{obs}$ such that $S_{Vol} = \{s_{t1}, s_{t2}, \dots, s_{Tobs}\}$. These historical states typically include position, velocity, acceleration, and heading angle. The future trajectory, represented as $T_{Vol} = \{T_{t1}, T_{t2}, \dots, T_{Pred}\}$, is then predicted over a defined horizon $T_{Pred}$. Moreover, while some TP approaches use only the VoI's tracking history, most learning-based methods also incorporate additional contextual information, such as the tracking history of surrounding agents, interaction patterns, road geometry, and intended maneuvers.

III. TRAJECTORY PREDICTION TAXONOMY

The classification and review of motion prediction, partic-
ularly trajectory prediction, have been largely explored in
the literature. In [33], the authors presented a taxonomy for
motion prediction methods for both pedestrians and vehi-
cles. It consists of three categories: (i) modeling approach
(physics-based or learning-based), (ii) output type (inten-
tion, unimodal/multimodal trajectory, or occupancy map),
and (iii) situational awareness. The latter reflects the infor-
mation level about the environment during the prediction.
This level ranges from unawareness of environmental changes
to awareness of the surrounding agent's interactions, the
scene, or contextual map information. Huang et al. [13]
introduced a slightly different taxonomy based on (i) the
prediction method (physics-based, ML-based, DL-based, or
Reinforcement Learning-based); (ii) the input contextual fac-
tors (physics-related, road-related, or interaction-related); and
(iii) the output type (unimodal/multimodal trajectory and in-
tention). An earlier survey by Lefèvre et al. [34] classified
SOTA vehicle motion prediction methods based on model-
ing assumptions, proposing three categories: (i) physics-based
models, (ii) maneuver-based models that account for driver
intention, and (iii) interaction-aware models that capture the
influence of surrounding vehicles.

Various surveys have focused on reviewing DL approaches
like Mozaffari et al. [15], who provided a detailed review
and classification of DL-based vehicle behavior prediction
methods. Their taxonomy is based on (i) input represen-
tation (VoI track history, surrounding agents track history,
bird's-eye view, or raw sensor data), (ii) output type (maneu-
ver, uni/multimodal trajectory, or occupancy map), and (iii)
the employed DL technique (RNN, CNN, or others). Further
<figure><img src="image_3.png" /><figcaption>**FIGURE 2.** The proposed taxonomy categorizes methods based on environment type, trajectory type, and the modeling approach used to enhance prediction, whether through lane structure, maneuvers, interactions, dynamics, or hybrid combinations.</figcaption></figure>

contributions on DL-based TP are provided in [20], [35], [36]. Liu et al. [35] reviewed and classified DL approaches using (i) data representation (images or continuous space states) of agent states, scene context, and outputs. (ii) The DL model design, which varies according to where it is applied (feature encoding, interaction modeling, or trajectory generation). Finally, (iii) the DL model training objective functions. This classification is particularly valuable for understanding how model architecture and training choices affect model performance. In contrast, Huang et al. [20] structure recent works according to (i) scene representation (grid- or graph-based), including past states of surrounding agents and map information, (ii) feature extraction method. These features cover individual properties of dynamic surrounding agents, interaction, and map information features. Finally, (iii) prediction ratio nality, which measures the semantic plausibility of prediction in real-world scenarios. Benrachou et al. [36] focused their review on interaction- and intention-based motion prediction methods and classified them by (i) prediction type (inten tion or interaction), (ii) employed algorithms (CNNs, RNNs, GNNs, or hybrid), and (iii) dataset type, distinguishing be tween top-down-view (collected with infrastructure-mounted sensors) and vehicle-view (collected with on-board sensors). A broader classification of ML-based vehicle TP methods is proposed by Bharilya et al. [37] comprising three classes: (i) conventional approaches (e.g., mathematical and probabilistic approaches), (ii) DL methods, and (iii) reinforcement learning methods.

Although previous surveys have proposed insightful classifications, many focus exclusively on DL-based methods [15], [20], [35], [36] or, more broadly, ML approaches [37]. In contrast, this paper provides a comprehensive review and classification of conventional and learning-based TP methods. First, following [13], [33], we acknowledge that the output type can vary from trajectory coordinates to intention classes or occupancy maps. However, this work focuses on methods that predict explicit trajectories, as defined in the previous section. Therefore, the first class of the proposed taxonomy, as illustrated in Fig. 2, is the trajectory output type that can

be either unimodal or multimodal. Furthermore, although the environment type is often overlooked in previous taxonomies, we believe that classifying methods based on the operating environment provides valuable insights about whether some methods are often used for specific environments. Thus, we categorize approaches by environment type: urban, highway, or mixed/unspecified. Lastly, we classify prediction models based on their underlying modeling approach, as in [34]. Our classification extends their three categories (physics-based, maneuver-based, and interaction-based) by adding lane-based methods, which explicitly incorporate road geometry into the prediction. We also identify hybrid approaches that combine two or more modeling approaches to improve predictive performance. In summary, the proposed taxonomy is structured along three classes: (i) trajectory type (unimodal vs. multimodal), (ii) environment type (urban, highway, or mixed), and (iii) modeling approach (physics-based, lane-based, maneuver-based, interaction-based, and hybrid).

# IV. ANALYSES OF SOTA MODELING APPROACHES

This section analyzes and classifies traditional and AI-based SOTA TP modeling approaches according to the proposed taxonomy.

## A. PHYSICS-BASED METHODS

Physics-based methods consist of using motion models to estimate the time evolution of a vehicle's state under the influence of different variables and by considering its physical laws. The literature distinguishes two types of motion models: kinematic and dynamic models. They can be used on their own for direct predictions or combined with filtering techniques like the Kalman Filter (KF) to reduce uncertainty, measurement errors, and sensor noise.

Kinematic-based prediction involves modeling vehicle motion without considering internal and external forces. Schubert et al. [14] defined two levels of kinematic motion models according to their complexity. i) The linear straight motion models like the constant velocity (CV) and the constant acceleration (CA) models, which assume, respectively, a constant velocity and constant acceleration over time. These models operate effectively in straight environments such as freeways, but fail in environments involving rotations and complex maneuvers. At the second level, there are curvilinear models, which can deal with rotations. They include Constant Turn Rate and Velocity (CTRV), Constant Turn Rate and Acceleration (CTRA) models, Constant Steering Angle and Velocity (CSAV), and Constant Steering Angle and Acceleration (CSAA). Collision avoidance and warning are common use cases for kinematic-based prediction. Lytrivis et al. [18] proposed a method that predicts trajectories for both the ego and surrounding vehicles to improve collision warning systems. Their approach combines a short-term path using a CTRA model and KF, as well as a long-term path based on road geometry. In [16], Mercat et al. introduced a reproducible single-vehicle prediction method that estimates future kinematic states and associated errors using a CV model and KF.
The method applies the KF's prediction-update-correction cycle to observed positions, then predicts future states using only the prediction step, as no future observations are available.

On the other hand, Dynamic models describe vehicle motion by incorporating internal and external forces and are particularly suited for high-speed scenarios or sharp path curvature [38]. Commonly used dynamic models in TP include the bicycle and Ackermann models. Like kinematic models, dynamic models are also applied to collision avoidance. Ammoun et al. [39] enabled early collision detection by predicting the trajectories of ego and surrounding vehicles using the bicycle model and a KF. The bicycle model simplifies vehicle representation by merging the front and rear wheels into single wheels located at the center of each axle [40]. In [39], two connected vehicles approached a crossroads, unaware of each other's presence. Each computed its own and the other's trajectory over a 10-second horizon, and Time to Collision (TTC) was computed to assess risk. A similar approach by Lin et al. [41] uses a bicycle model and KF to predict vehicle states as a first step in computing Time to Lane Crossing (TLC) for lane departure detection.

Filtering techniques are commonly used to handle uncertainties in motion models and sensor data and improve the accuracy of physics-based prediction methods. The Kalman filter [42] is among the most widely adopted Bayesian filters, offering a recursive solution for linear systems under Gaussian noise assumptions. For nonlinear systems, variants like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are often employed. In vehicle TP, KF is typically applied to predict future states using a motion model and update them with new measurements. Several studies [16], [18], [43] have combined KF with motion models for enhanced trajectory estimation. Beyond Kalman-based methods, more flexible filters, such as the Particle Filter (PF), also known as Sequential Monte Carlo (SMC) [44], are used for systems with non-Gaussian and nonlinear dynamics. For instance, Okamoto et al. [45] applied SMC with a dynamic model to predict surrounding vehicle trajectories for threat assessment, incorporating driver maneuver estimation via a random forest classifier to enhance the prediction.

In conclusion, physics-based models are typically used for short-term predictions, as their constant assumptions can lead to imprecise predictions in long-term predictions [46]. Moreover, they do not incorporate contextual information and cannot be generalized to all driving scenarios since each model is designed for specific conditions. However, their simplicity and low computational cost explain why they are still used in real-time TP applications. CA and CV are mainly used in straight environments such as highways; for environments involving rotations, these models, or others, can be employed: CTRV, CTRA, CSAV, and CSAA. It is also worth noting that dynamic models are generally used in control applications rather than TP like in [47]. Kinematic models are often chosen because they are more straightforward and focus more on the motion itself rather than the forces behind it. As for the filtering techniques, KF is widely adopted for

its computational efficiency, making it suitable for real-time driving applications. However, its applicability is limited to linear systems with Gaussian noise, which may not accurately capture real-world dynamics. While its extensions—Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF)— address non-linearity, they still assume Gaussian noise. KF and its variants are unable to support multimodal prediction as they assume a unimodal state distribution. In contrast, PF supports non-linear, non-Gaussian models and can handle multimodal prediction. This flexibility comes at a computational cost, particularly as the number of particles increases.

Learning-based methods improve long-term accuracy and generalization over physics-based models but often lack interpretability and kinematic feasibility. To address this, studies like Cui et al. [22] add a kinematic layer to the learning model, predicting acceleration and steering to ensure physically plausible trajectories.

## B. INTERACTION-BASED METHODS

Interaction-based (or “interaction-aware”) methods take into account how the behavior of surrounding agents influences the VoI’s future motion. These interactions can be modeled using either map-based or map-free approaches. Map-free methods rely solely on the track states of nearby agents, such as relative positions, to construct interaction models. For example, Schmidt et al. [48] proposed CRAT-Pred, a multimodal trajectory prediction method that employs a crystal graph convolutional network (originally used to model atomic interactions) to model vehicles as nodes and interactions, defined as relative distances between vehicles, as edges. A multi-head self-attention mechanism further distinguishes and gives a score to each modeled interaction. Similarly, GRIP++ [49] models interactions using a spatiotemporal graph where edges are established between agents that are spatially close (within a fixed distance threshold) and across time steps to capture motion history. These graphs are processed by graph convolutional layers and a GRU-based encoder-decoder architecture to generate multi-agent future trajectories. Aligned with map-free, interaction-aware trajectory prediction approaches, Zhang et al. [50] propose a TCN-LSTM model that learns vehicle interactions directly from historical trajectories to support risk-aware motion planning. Their model takes as input the past motion of the target and surrounding vehicles and uses temporal convolutions with a social pooling module to encode inter-vehicle dependencies. It then outputs multimodal Gaussian trajectory distributions using an LSTM decoder.

On the other hand, map-based methods take advantage of more structured and contextual information coming from the map to model interactions. For instance, AutoBots [51] employs a transformer-based encoder-decoder architecture that encodes temporal and social features through multi-head attention to generate multimodal trajectory distributions in a single pass, unlike conventional methods based on auto-regressive sampling. VectorNet [26] adopts a
hierarchical graph-based approach by vectorizing HD maps and agent trajectories into polylines using a local graph network and modeling their interactions through a global interaction graph. Map features such as lane boundaries, centerlines, and crosswalks are encoded as vectors with semantic attributes and integrated into the graph structure to capture agent-map interactions via self-attention mechanisms. Similarly, EMP [28] is a lightweight, map-based method that encodes agent past states and HD map lane centerlines using standard transformer blocks. It models interactions via attention mechanisms applied to a concatenated scene representation formed from agent and lane embeddings, allowing the model to align trajectory predictions with the underlying road geometry.

Whether map-based or not, interactions are generally modeled either using graph-based representations, where agents are represented as nodes, and their interactions as edges, as in [48] and [49], or using an attention-based solution to learn social and temporal dependencies between vehicles [51], [28] and [52], or using a pooling-based solution that uses feature aggregation techniques to combine input information into a social feature, as in [53] and [54].

In summary, map-free models rely solely on agent states, making them lightweight and suitable for unstructured environments, such as rural areas. However, they may struggle in situations where road geometry heavily influences interactions, such as intersections and freeways, particularly during lane merges or exit ramps where spatial constraints are important. In contrast, map-based methods incorporate HD map features like lane centerlines and road topology to enhance interaction modeling. While often more accurate, they can be demanding in terms of pre-processing, as they require detailed map data. This data needs to be updated and processed within real-time constraints, which can be difficult, especially if map information is outdated or unavailable.

## C. MANEUVER-BASED METHODS

In maneuver-based (or intention-based) TP methods, future vehicle states are estimated by first recognizing the intended driving maneuver, which is generally approached as a classification task. Commonly recognized maneuver classes include left lane change, right lane change, and lane keeping. These methods can be categorized by how they model and integrate maneuvers into the prediction pipeline, either through probabilistic models and classical ML or using DL techniques.

Probabilistic maneuver-based methods first infer the most likely driving intent using statistical models, then condition TP on the recognized maneuver to generate diverse trajectories. A standard probabilistic model is the Gaussian process, a Bayesian process that offers a distribution over possible predictions and their associated uncertainties given observed data. Each finite collection of these possible predictions follows a joint Gaussian distribution [55]. Tran et al. [56] trained four three-dimensional Gaussian process regression models to

represent the four possible maneuvers at intersections (turn-left, turn-right, go-straight, and stop-and-go). Given a new set of motion data, a likelihood analysis is performed to detect the regression model that corresponds best to the motion data. Based on the recognized maneuver, the trajectories of each vehicle are predicted using PF. Another probabilistic method is the Bayesian Network (BN), an acyclic probabilistic graphical model [57] that can capture dependencies between variables such as velocity, position, and orientation. Schreier et al. [58] used a BN for maneuver recognition. A specific physics-based prediction model is applied for each recognized maneuver to compute a range of possible trajectories associated with their probabilities. A joint probability density function over all trajectories is then computed by weighting each maneuver-specific trajectory distribution with its corresponding maneuver probability.

On the other hand, DL-based maneuver-based trajectory prediction methods typically combine learned maneuver classification with trajectory prediction. It is also quite common to consider the interaction between vehicles for better prediction. Mersch et al. [59] proposed a spatio-temporal CNN architecture for TP in highway environments. Their approach is a three-stage pipeline. First, a 3D tensor describing the motion of the VoI and up to seven surrounding vehicles is processed by the CNN model for feature aggregation. The extracted features are then passed to two separate branches: a classification module that predicts the vehicle's maneuver (straight line, left lane change, or right lane change) at each future time step and a regression module that uses the predicted sequence of maneuvers to generate the vehicle's future trajectory. Deo et al. [60] introduced a single-vehicle multimodal maneuver-based TP method using the tracking history of the VoI and its six surrounding vehicles. Whereas Mersch et al. [59] focused solely on lateral maneuver classification (left lane change, lane keeping, and right lane change), Deo et al. [60] also considered longitudinal ones (normal driving and braking). Their approach consists of encoding the historical tracking states and relative positions of the VoI and its surroundings into a context vector using an LSTM encoder. The resulting encoded vector is merged with another LSTM-encoded vector that represents maneuver classes. These vectors are then processed by an LSTM decoder, which predicts the future positions of vehicles for each maneuver and assigns a probability to each maneuver class. Other interaction-aware maneuver-based methods like [53] and [54] are discussed in the hybrid section.

To conclude, maneuver-based prediction is generally used for long-term predictions since it considers the driver's intentions. In multimodal prediction, considering maneuvers helps narrow down possible future trajectories by limiting them to those feasible in terms of the predicted maneuver. However, in such a prediction type, the accuracy of predicted trajectories depends heavily on the quality of maneuver classification. In addition, maneuver-based methods require supplementary data for maneuver detection, which is usually related to the lanes.
## D. LANE-BASED METHODS

Road-based methods operate under the assumption that the vehicle will follow the road geometry [43]. These methods involve modeling the road geometry and using it in the prediction process. They are also referred to as lane-based methods [61] or lane-aware methods [62] and can be applied when having road information such as lanes and markings as inputs. This information is typically derived from either rasterized or vectorized maps. Rasterized maps, being image-based, allow deep learning models to extract spatial features but are computationally expensive and resolution-dependent [61]. In contrast, vectorized maps use geometric objects (points, lines, polygons), preserve lane topology more effectively, and are computationally more efficient.

Kawasaki et al. [61], Kim et al. [62], and Liang et al. [63] employed vectorized maps in their lane-based prediction approach. Kawasaki et al. [61] proposed a multimodal prediction method called LAMP-Net to predict the future trajectory of surrounding vehicles and their corresponding probability by assuming that the trajectory is one of the center lines of existing lanes. The particularity of this work is the versatility of the model, which handles all lane numbers and shapes by incorporating a new lane feature that reflects the geometric relationship between each lane and the target vehicle. LAMP-Net is an LSTM-based model that incorporates a tailored KF module based on LSTM to perform parameter estimation. On the other hand, Kim et al. [62] introduced LaPred, a Lane-aware Prediction method, which also incorporates a lane feature that represents the relationship not only between the target agent and the lane but also with the surrounding agent that has the most influence on the target agent. Unlike methods that directly use raw vectorized maps, LaPred extracts instance-level lane information from semantic vectorized maps, ensuring a structured representation of the relationship between the lane, the agent, and the nearby agent. LaPred [62] consists of using 1D-CNN and LSTM for feature encoding, and the encoded features are merged into a trajectory-lane feature that serves as input to a CNN-based multimodal trajectory model. Whereas LaPred treats each lane independently, LaneGCN [63] consists of constructing a lane graph from the map that models the relationships between lanes and extracting actor features from past trajectories. Using the extracted features and the lane graph, LaneGCN builds four interconnected graphs: actor-actor (A2A), actor-lane (A2L), lane-actor (L2A), and lane-lane (L2L). The prediction module then fuses these graphs to generate multimodal TPs.

Overall, lane-based predictions tend to inherently follow realistic paths by complying with lane constraints, assuming the lane representation accurately reflects the environment. However, in specific environments with many possibilities, such as intersections, it becomes more difficult to predict the vehicle's future trajectory based on the shape of the road. Moreover, Lane-based methods are highly dependent on the availability of road information, which is usually derived from HD map data but is not always available, as in some

environments, lane markings are not always visible or absent. In addition, online processing of traffic information requires additional computational resources.

## E. HYBRID METHODS

While many TP methods rely on a single context representation, hybrid approaches combine multiple modeling approaches to benefit from their complementary strengths. Xie et al. [64] introduced the Interactive Multiple Model TP (IMMTP) approach, which fuses a physics-based model (CTRA + UKF) with a maneuver-based model (DBN). Since physics-based models are accurate in the short-term horizon and maneuver-based models are more adapted to long-term prediction, IMMTP leverages both strengths by recursively estimating the probability of each model based on its prediction variance. The final trajectory is then computed as a weighted combination of both model outputs, and the weights are dynamically adjusted according to each model's probability. Notably, the method achieved an average computation time of 78 ms on an Intel Core i7 with 8 GB RAM at 2.2 GHz. Similarly, Houenou et al. [65] combined a rule-based Maneuver Recognition Module (MRM) with a Constant Yaw Rate and Acceleration physics-based motion model. It identifies the vehicle maneuver by comparing its path (modeled locally as an arc) to the center lines of the road lanes. If the path aligns with the lane center, it is a keep lane; if it diverges, the vehicle changes the lane. The final output is a weighted combination of maneuver- and physics-based trajectory, using a cubic spline weight function, assuming access to lane geometry from a perception system.

Recent methods have also explored hybrid approaches that combine maneuver and interaction modeling. Deo et al. [53] proposed a multimodal LSTM-based model for freeway scenarios, using an improved version of social pooling layers introduced in [66] and max-pooling layers to encode spatial interactions from surrounding vehicles. The resulting social context is combined with the Vol's dynamics and passed to an LSTM decoder to predict position distributions across six maneuver classes. Similarly, Hasan et al. [54] proposed a maneuver-aware pooling mechanism that improves vehicle interaction modeling by incorporating polar trajectory representations, vehicle orientation, and radial velocity. This pooling strategy is embedded into a generative LSTM encoder-decoder model, where maneuver recognition is performed using two softmax layers for location and acceleration maneuver classes. The resulting maneuver probabilities are fused with interaction features to generate multimodal trajectory predictions.

In addition to maneuver modeling, Polychronopoulos et al. [43] also account for the vehicle dynamics and road geometry in their proposed rule-based TP method for collision warning, ACC, and vision-enhancement applications. Their approach fuses predictions from dynamic models, such as CA, CTR, and CTRA, combined with a KF, as well as predictions derived from road geometry. A maneuver detection module
<table><caption>TABLE 1. Comparative Table of SOTA Vehicle TP Methods for Urban and Semi-Urban Environments, Considering Output Type, Prediction Approach, Hardware and Sensor Setups, Evaluation Settings (Datasets and Metrics), Prediction Horizon in Seconds (PH), and Source Code Availability</caption><thead><tr><th>Output</th><th>Ref</th><th>Prediction</th><th>HW</th><th>Sensors</th><th>Data</th><th>Metrics</th><th>PH</th><th>Code</th></tr></thead><tbody><tr><td>Unimodal</td><td>[18]<br>2008</td><td>Physics (KF + CTRA) and<br>road-based</td><td>-</td><td>GPS, Inertial<br>sensor, wireless<br>communication<br>device</td><td>Simulated data</td><td>Position error</td><td>3s</td><td>No</td></tr><tr><td>Unimodal</td><td>[22]<br>2020</td><td>Physics-based (DKM)</td><td>Self-driving<br>vehicle</td><td>-</td><td>240 hours of<br>collected data</td><td>Position and heading<br>errors, and kinematic<br>infeasibility</td><td>6s</td><td>No</td></tr><tr><td>Unimodal</td><td>[65]<br>2013</td><td>Physics (CYRA) and<br>maneuver-based (rule-based)</td><td>PKU Omni<br>Smart<br>Sensing<br>Vehicle</td><td>-</td><td>Collected data<br>in Beijing</td><td>Mean prediction error</td><td>4s</td><td>No</td></tr><tr><td>Multimodal</td><td>[56]<br>2014</td><td>Maneuver-based (GP and PF)</td><td>A research<br>vehicle<br>(AnnieWAY)</td><td>Laser scanner<br>sensor Velodyne<br>HDL-64E S2</td><td>Collected data<br>in Karlsruhe<br>and Germany</td><td>-</td><td>-</td><td>No</td></tr><tr><td>Multimodal</td><td>[61]<br>2020</td><td>Road-based (LSTM and<br>tailored-KF)</td><td>-</td><td>Employed sensors<br>in AV1 (LiDARs<br>and cameras)</td><td>Collected data<br>from Tokyo<br>and AV1</td><td>Predicted lane error,<br>oracle lane error,<br>ADE, and FDE</td><td>4s</td><td>No</td></tr><tr><td>Multimodal</td><td>[62]<br>2021</td><td>Lane-based (1D-CNN and<br>LSTM-based feature<br>encoding, probabilistic-based<br>feature construction, and<br>CNN-based prediction<br>generator)</td><td>-</td><td>Employed sensors<br>in AV1 and in<br>NuScenes<br>(cameras, LiDAR,<br>radars, GPS, and<br>IMU)</td><td>AV1 and<br>NuScenes</td><td>ADE<sub>k</sub>, FDE<sub>k</sub>,<br>k ∈ {1, 5, 10, 15}</td><td>6s<br>and<br>3s</td><td>No</td></tr><tr><td>Multimodal</td><td>[63]<br>2020</td><td>Lane-based (Graph and<br>attention -based)</td><td>4 TITAN-X<br>GPUs</td><td>Employed sensors<br>in AV1</td><td>AV1</td><td>minADE<sub>k</sub>,<br>minFDE<sub>k</sub>, and MR<sub>k</sub>,<br>k ∈ {1, 6}</td><td>3s</td><td>Yes</td></tr><tr><td>Multimodal</td><td>[48]<br>2022</td><td>Interaction-based (crystal<br>graph CNN and multi-head<br>self-attention mechanism)</td><td>-</td><td>Employed sensors<br>in AV1</td><td>AV1</td><td>minADE<sub>k</sub>,<br>minFDE<sub>k</sub>, and MR<sub>k</sub>,<br>k ∈ {1, 6}</td><td>3s</td><td>Yes</td></tr><tr><td>Multimodal</td><td>[51]<br>2022</td><td>Interaction-based<br>(transformer-based<br>encoder-decoder architecture)</td><td>Nvidia<br>Geforce GTX<br>1080Ti GPU</td><td>Employed sensors<br>in AV 1,<br>NuScenes, and<br>TrajNet++</td><td>NuScenes,<br>AV1, and<br>TrajNet++</td><td>MR<sub>k</sub>, OffRoad Rate,<br>DAC, minADE<sub>k</sub>,<br>minFDE<sub>k</sub>, collision<br>number</td><td>6s<br>and<br>3s</td><td>Yes</td></tr><tr><td>Multimodal</td><td>[28]<br>2024</td><td>Interaction- and lane-based<br>(Transformer encoders, Mini<br>PointNet lane encoder,<br>MLP/Transformer decoder)</td><td>V100,<br>RTX2080Ti,<br>RTX4090<br>GPUs</td><td>Employed sensors<br>in AV1 and AV2</td><td>AV1 and AV2</td><td>MR<sub>k</sub>, minADE<sub>k</sub>,<br>minFDE<sub>k</sub>,<br>brier-minFDE<sub>k</sub>,<br>k ∈ {1, 6}, latency<br>and training time,<br>params number</td><td>3s<br>and<br>6s</td><td>Yes</td></tr><tr><td>Multimodal</td><td>[26]<br>2020</td><td>Interaction (GNN +<br>self-attention) and lane-based<br>(vectorized polylines)</td><td>8 GPUs</td><td>Employed sensors<br>in AV1 and<br>Waymo</td><td>AV1 and<br>Waymo</td><td>ADE, Displacement<br>Error at 1s, 2s, 3s,<br>parameters number,<br>FLOPs</td><td>3s</td><td>Yes</td></tr></tbody></table>

classifies the driver's intent using lateral velocity thresholds. The final path is predicted using the Fused Combinational Dynamics and Road Boundaries (F-CDRB) model, which blends dynamic and road-based predictions using weights based on maneuver class, vehicle state consistency, and lateral deviation. This fusion strategy improved prediction accuracy, reducing RMSE by about 10% over using individual models.

## F. CONCLUSION

In addition to the detailed analysis of each modeling approach, Tables 1, 2, and 3 present a structured comparison of vehicle TP methods across urban/semi-urban, highway, and mixed or unspecified environments, respectively. The comparison includes output types, TP modeling approaches, employed

hardware and sensors, evaluation settings (datasets and metrics), prediction horizons, and code availability. These tables aim to identify potential correlations between environment types, output types, and modeling approaches to support and motivate the proposed taxonomy. They also provide insights into the most frequently employed hardware architectures and sensor configurations in the literature.

Regarding output type, physics-based and probabilistic maneuver-based TP methods typically produce unimodal trajectories, reflecting a single deterministic or probabilistic dominant path. This aligns with their design, which often relies on predefined motion models or maneuver classes. In contrast, AI-based methods, particularly deep learning methods, are more likely to generate multimodal
<table><caption>TABLE 2. Comparative Table of SOTA Vehicle TP Methods for Highway Environments, Considering Output Type, Prediction Approaches, Hardware and Sensor Setups, Evaluation Settings (Datasets and Metrics), Prediction Horizon in Seconds (PH), and Source Code Availability</caption><thead><tr><th>Output</th><th>Ref</th><th>Prediction</th><th>HW</th><th>Sensors</th><th>Data</th><th>Metrics</th><th>PH</th><th>Code</th></tr></thead><tbody><tr><td>Multimodal</td><td>[45] 2017</td><td>Physics and maneuver based (PF + RNN)</td><td>-</td><td>-</td><td>Simulation data using CarSim</td><td>TTC</td><td>-</td><td>No</td></tr><tr><td>Multimodal</td><td>[59] 2021</td><td>Maneuver-based (CNN)</td><td>Intel Xeon W-2133 CPU and an Nvidia Quadro RTX 5000 GPU</td><td>Employed sensors in HighD (mounted camera on a drone) and NGSIM (mounted cameras on buildings)</td><td>HighD and NGSIM (US-101 and I-80)</td><td>RMSE</td><td>5s</td><td>No</td></tr><tr><td>Multimodal</td><td>[53] 2018</td><td>Interaction (social pooling) and maneuver based (LSTM + CNN)</td><td>-</td><td>Employed sensors in NGSIM</td><td>NGSIM US-101 and I-80</td><td>RMSE and NLL</td><td>1-5s</td><td>Yes</td></tr><tr><td>Multimodal</td><td>[60] 2018</td><td>Interaction and maneuver-based (LSTM)</td><td>-</td><td>Employed sensors in NGSIM</td><td>NGSIM US-101 and I-80</td><td>RMSE</td><td>5s</td><td>No</td></tr><tr><td>Multimodal</td><td>[54] 2021</td><td>Interaction and maneuver-based (LSTM encoder-decoder)</td><td>-</td><td>Employed sensors in NGSIM</td><td>NGSIM US-101 and I-80</td><td>RMSE</td><td>1-5s</td><td>Yes</td></tr><tr><td>Both</td><td>[16] 2020</td><td>Physics-based (KF and CV)</td><td>-</td><td>Employed sensors in NGSIM</td><td>NGSIM US-101 and I-80</td><td>RMSE, FDE, MR, NLL</td><td>1-5s</td><td>Yes</td></tr><tr><td>Unimodal</td><td>[41] 2000</td><td>Physics-based (Bicycle model and KF)</td><td>-</td><td>UM prototype [67]</td><td>5 different simulation experiments</td><td>TLC</td><td>4s</td><td>No</td></tr></tbody></table>

<table><caption>TABLE 3. Comparative Table of SOTA Vehicle TP Methods for Mixed or Unspecific Environments, Considering Output Type, Prediction Approaches, Hardware and Sensor Setups, Evaluation Settings (Datasets and Metrics), Prediction Horizon in Seconds (PH), and Source Code Availability</caption><thead><tr><th>Environment</th><th>Output</th><th>Ref</th><th>Prediction</th><th>HW</th><th>Sensors</th><th>Data</th><th>Metrics</th><th>PH</th><th>Code</th></tr></thead><tbody><tr><td>Highways + extra urban + rural roads</td><td>Unimodal</td><td>[43] 2007</td><td>Physics-based, road-based and maneuver-based</td><td>Two test vehicles from Volvo Cars and Fiat Research Centre</td><td>Infrared camera and a high resolution radar</td><td>Collected data</td><td>RMSE</td><td>3-4s</td><td>No</td></tr><tr><td>highway + ring road + airport express lane + city road</td><td>Unimodal</td><td>[64] 2017</td><td>Physics-based (CTRA and UKF) and maneuver-based (DBN)</td><td>A vehicle platform</td><td>360 degree laser, LiDAR, 4 external cameras, 1 internal camera</td><td>Collected data in Beijing</td><td>Average prediction error</td><td>0.5-8s</td><td>No</td></tr><tr><td>Highways + urban environments</td><td>Multimodal</td><td>[49] 2020</td><td>Interaction-based (Graph-based)</td><td>4.0 GHz Intel Core i7 CPU, 32GB Memory, and a NVIDIA Titan Xp Graphics Card</td><td>Employed sensors in NGSIM and in ApolloScape (laser scanners, camera, IMU/GNSS)</td><td>NGSIM (US-101 and I-80) and ApolloScape</td><td>RMSE, WSADE, ADE, FDE and WSFDE</td><td>5s</td><td>Yes</td></tr><tr><td>Unspecific</td><td>Multimodal</td><td>[58] 2014</td><td>Maneuver-based (BN)</td><td>-</td><td>-</td><td>Simulation data (Car-maker)</td><td>TTC and TTCCP<sup>1</sup></td><td>-</td><td>No</td></tr></tbody></table>

predictions. Regarding the type of environment, we observe that maneuver-based methods, especially those based on DL, are predominantly evaluated in highway settings. This is likely due to the structured nature of highways, where lane markings are more precise and more consistently annotated, and maneuvers (e.g., lane changes, merges, highway entries/exits) are more discrete and well-defined. These characteristics make

highways particularly suitable for maneuver classification and modeling.

Regarding hardware deployment, only physics-based and probabilistic models, from the presented methods, have been directly evaluated on onboard vehicles. However, the literature

<footnote n="1">¹TTCCP: Time-To-Critical-Collision-Probability</footnote>
<table><caption>TABLE 4. Summary of SOTA Displacement Metrics for Trajectory Prediction Across Unimodal, Multimodal, and Joint Multi-Agent Contexts</caption><thead><tr><th>Metric</th><th>Ref</th><th>Definition</th><th>Equation</th></tr></thead><tbody><tr><td>MAE</td><td>[68]</td><td>Mean absolute difference between predicted and GT positions.</td><td>MAE = <sup>1</sup>&frasl;<sub>n</sub> &sum;<sub>i=1</sub><sup>n</sup> |a<sub>i</sub><sup>gt</sup> - x<sub>i</sub><sup>pred</sup>|</td></tr><tr><td>ADE</td><td>[69]</td><td>Mean L2 distance between predicted and GT positions</td><td>ADE = <sup>1</sup>&frasl;<sub>n</sub> &sum;<sub>i=1</sub><sup>n</sup> &radic;((X<sub>i</sub><sup>pred</sup> - X<sub>i</sub><sup>gt</sup>)<sup>2</sup> + (Y<sub>i</sub><sup>pred</sup> - Y<sub>i</sub><sup>gt</sup>)<sup>2</sup>)</td></tr><tr><td>FDE</td><td>[69]</td><td>L2 distance between the final predicted position (i = n) and the final GT position.</td><td>FDE = &radic;((X<sub>n</sub><sup>pred</sup> - X<sub>n</sub><sup>gt</sup>)<sup>2</sup> + (Y<sub>n</sub><sup>pred</sup> - Y<sub>n</sub><sup>gt</sup>)<sup>2</sup>)</td></tr><tr><td>RMSE</td><td>[53], [60]</td><td>Square root of the average squared difference between predicted and GT positions.</td><td>RMSE = &radic;(<sup>1</sup>&frasl;<sub>n</sub> &sum;<sub>i=1</sub><sup>n</sup> ((X<sub>i</sub><sup>pred</sup> - X<sub>i</sub><sup>gt</sup>)<sup>2</sup> + (Y<sub>i</sub><sup>pred</sup> - Y<sub>i</sub><sup>gt</sup>)<sup>2</sup>))</td></tr><tr><td>minADE<sub>k</sub>, minFDE<sub>k</sub></td><td>[70]</td><td>Minimum FDE and ADE values among the k predicted trajectories; in some cases, such as AV 1 [71], minFDE<sub>k</sub> is computed directly, but minADE<sub>k</sub> as the ADE of the trajectory with the minimum FDE.</td><td>minADE<sub>k</sub> = <sup>min</sup>&frasl;<sub>k &isin; {1,...,K}</sub> ADE<sub>k</sub><br>minFDE<sub>k</sub> = <sup>min</sup>&frasl;<sub>k &isin; {1,...,K}</sub> FDE<sub>k</sub></td></tr><tr><td>brier-minFDE<sub>k</sub>, brier-minADE<sub>k</sub></td><td>[71], [72]</td><td>These metrics add (1 - p<sub>k</sub>)<sup>2</sup> to minFDE<sub>k</sub> and minADE<sub>k</sub> where p<sub>k</sub> is the probability of the k trajectory.</td><td>brier-minADE<sub>k</sub> = (1 - p<sub>k</sub>)<sup>2</sup> + minADE<sub>k</sub><br>brier-minFDE<sub>k</sub> = (1 - p<sub>k</sub>)<sup>2</sup> + minFDE<sub>k</sub></td></tr><tr><td>p-minADE<sub>k</sub>, p-minFDE<sub>k</sub></td><td>[71]</td><td>Penalize low-probability predictions by adding a limited negative log-probability term (bounded by -log(Cte)) minADE<sub>k</sub> and minFDE<sub>k</sub>, respectively. (e.g., Cte = 0.05 in AV 1 [71])</td><td>p-minADE<sub>k</sub> = min(-log(p<sub>k</sub>), -log(Cte)) + minADE<sub>k</sub><br>p-minFDE<sub>k</sub> = min(-log(p<sub>k</sub>), -log(Cte)) + minFDE<sub>k</sub></td></tr><tr><td>WFDE, WADE</td><td>[73]</td><td>Weighted extensions of ADE and FDE that assign type-specific weights to each agent class (vehicles, pedestrians, bicyclists) to account for their distinct motion dynamics</td><td>WSADE = D<sub>v</sub> &middot; ADE<sub>v</sub> + D<sub>p</sub> &middot; ADE<sub>p</sub> + D<sub>b</sub> &middot; ADE<sub>b</sub><br>WSFDE = D<sub>v</sub> &middot; FDE<sub>v</sub> + D<sub>p</sub> &middot; FDE<sub>p</sub> + D<sub>b</sub> &middot; FDE<sub>b</sub></td></tr></tbody></table>

rarely describes the onboard hardware architecture employed. In contrast, AI-based methods are almost exclusively developed and evaluated on high-performance architectures. This highlights a notable gap in the literature: the lack of evaluation of AI-based TP models on embedded or resource-constrained hardware, which is critical for real-time deployment in vehicles. Finally, concerning evaluation metrics, specific datasets have standard metric conventions. For example, NGSIM often uses RMSE, Argoverse commonly reports metrics are minADE<sub>k</sub>, minFDE<sub>k</sub>, DAC<sub>k</sub>, and MR<sub>k</sub>. As for ApolloScape, it typically uses WSADE and WSFDE.

## V. DATASETS, METRICS, AND PREDICTION METHOD SELECTION

This section reviews the existing SOTA evaluation metrics and datasets and outlines the selection of the datasets, metrics, and prediction methods used for the evaluation conducted in this work. Prior to proceeding, the following notations will be used in the remainder of the paper: we refer to the predicted trajectory as a vector of predicted positions (Xpred, Ypred) and the Ground Truth (GT) trajectory as a vector of GT positions (Xgt, Ygt).

### A. EVALUATION METRICS

Given the focus is on trajectory prediction, we evaluate model accuracy using trajectory-based metrics and assess deployment feasibility through model complexity and computational performance metrics.

**Algorithmic performance:** the metric classification followed in this paper for the algorithmic performance is adopted from [20]. It groups trajectory-based metrics into displacement metrics for spatial accuracy and ability metrics for assessing prediction feasibility.

Displacement metrics vary depending on the prediction context: unimodal, multimodal, or joint multi-agent. In the unimodal case, where a single trajectory is predicted for one vehicle, metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Average Displacement Error (ADE), and Final Displacement Error (FDE) are commonly used. ADE and FDE can also be applied to multimodal predictions where evaluation is conducted on the most probable predicted trajectory, as in [61]. For fully multimodal approaches that generate and evaluate multiple trajectories, extended metrics such as minADE<sub>k</sub>, minFDE<sub>k</sub>, p-minADE<sub>k</sub>, p-minFDE<sub>k</sub>, and their brier score variants capture better performance across the k number of possible future trajectories (modes). For joint multi-agent prediction, which involves forecasting the trajectories of multiple agents simultaneously, commonly used evaluation metrics include the average of ADE and FDE for unimodal predictions, the average of minADE<sub>k</sub>, minFDE<sub>k</sub> and their variations for multimodal predictions, the Weighted Sum of Average Displacement Error (WSADE), and the Weighted Sum of Final Displacement Error (WSFDE). Table 4 summarizes SOTA displacement metrics across the three prediction contexts, including their definitions, equations, and some references where they are employed.

Complementing displacement metrics, ability metrics assess the plausibility and feasibility of predicted trajectories, with commonly used metrics including Miss Rate (MR) and its variants, Drivable Area Compliance (DAC), and Negative Log-Likelihood (NLL). Miss rate, as used in [74], [75], measures the proportion of missing sequences where the endpoint error of predicted trajectories exceeds a defined threshold $\sigma$. In AV1 [71], a sequence is considered a miss if FDE (or minFDE in multimodal cases) > $\sigma$, where $\sigma = 2$ m. Schmidt et al. [76] proposed Lane Miss Rate (LMR), a variant of MR that measures how often predicted trajectories deviate
from the correct lane. Unlike MR, LMR can detect cases where predictions fall within acceptable spatial thresholds but in incorrect lanes. Additionally, the Argoverse 1 [71] evaluation script includes a probabilistic MR variant that incorporates the confidence $p_i$ associated with each predicted trajectory. Its expression is defined below, and N is the number of sequences.

$$p\text{-MR} = \frac{1}{N} \sum_{i=1}^{N} \left( \begin{array}{ll} 1.0 & \text{if } \text{FDE}_i > \text{threshold} \\ 1.0 - p_i & \text{otherwise} \end{array} \right) \quad (1)$$

On the other hand, drivable Area Compliance, as described in [35] and used in [51], [69], evaluates the realism of predicted trajectories by measuring the proportion of predicted trajectories that remain within the drivable area (DA). In the AV 1 benchmark, DA is defined using a binary raster map where pixels labeled 1 are drivable, and those labeled 0 are not drivable. World coordinates of predicted points are converted to pixel indices and checked against this map. A trajectory is compliant if all its points lie within the drivable area. DAC is then computed as the ratio of compliant trajectories per sequence, averaged over all sequences.

To assess the quality of probabilistic predictions, one effective metric is the Negative Log Likelihood. It measures, as demonstrated in the works of [53] and [16], how well the predicted distribution aligns with the GT. In vehicle TP, NLL quantifies the likelihood of the GT under the predicted distribution Y.

$$NLL = -\log(Y) \quad (2)$$

**Model complexity:** the complexity of DL models is a critical factor to consider when deploying them on constrained embedded architectures. The primary metrics used to assess model complexity include the total number of parameters and the model size, which reflects the required memory for model storage. And the number of floating-point operations (FLOPs) per forward pass or/and the count of multiply-and-accumulate (MAC) operations. FLOPs represent the total arithmetic operations the model performs for a single input sample during inference. In contrast, the number of MAC operations indicates the total count of multiplication-and-addition pairs necessary to execute a forward pass of the model on a single input sample. A reduced number of FLOPs or MACs indicates a faster inference time since the model runs fewer computations for each sample. In DL models, model size, number of parameters, FLOPs, and MACs can be optimized through several techniques [24], including model pruning, quantization, and redesign of the network structure. For instance, by substituting the LSTM encoder modeling vehicle dynamics with temporal convolutional networks and depthwise convolutions, DeepTrack [27] significantly reduced computational complexity, decreasing the number of model parameters by 43.75% and the number of MACs by 22.37% compared to the LSTM-based baseline introduced in [77].

**Computational performance:** for the majority of ADAS applications, scene understanding, decision-making, and control

actions must be performed in real time. As an integral part of this process, TP must also operate under strict temporal constraints. However, the evaluation of real-time performance is often limited to inference time alone, as seen in works such as [28] and [29], overlooking other computational overheads such as preprocessing and data transfer times between CPU and GPU. Moreover, in realistic deployments, TP processes incoming information sequentially, as data in real-time driving is provided frame by frame rather than in large batches. To better align with real constraints, we propose measuring and reporting the average preprocessing time, CPU-to-GPU transfer time, inference time, GPU-to-CPU transfer time, and total processing time, all using a batch size of one. The primary motivation for measuring the total processing time is to ensure TP can provide outputs to downstream components in time, enabling rapid and safe decision-making. Lin et al. [78] emphasize that the latency of the end-to-end system (from perception to control) must be under 100 ms with a frame rate exceeding 10 Hz to outperform human reaction times and adapt rapidly to dynamic traffic conditions. As a component of the end-to-end system, TP must execute within the 100 ms latency limit. Xu et al. [79] demonstrated that their ContextVAE model achieves real-time inference with a latency of less than 30 ms for predicting trajectories of multiple agents in a scene, evaluated on an NVIDIA V100 GPU. This includes generating trajectory samples for a batch size of 32. Another example evaluated on the same architecture (NVIDIA V100 GPU) is provided by Prutsch et al. [28], who report inference times of 28 ms for EMP-M and 37 ms for EMP-D, each measured while predicting a batch of 32 scenarios. Based on these representative benchmarks, we define the real-time constraint for the trajectory prediction module to be within 20–30 ms to ensure responsiveness in safety-critical ADAS applications.

## B. EVALUATION DATASETS

This section reviews SOTA datasets for vehicle TP, which can be broadly categorized by viewpoint [36]: (i) top-down-view datasets, captured using sensors on infrastructure or drones, and (ii) vehicle-view datasets, recorded from onboard vehicle sensors. While this study focuses on vehicle-view datasets, two widely used top-down datasets are also included to provide an overall overview of the existing datasets (NGSIM and HighD).

Top-down-view datasets are advantageous for avoiding occlusions and identity switching during tracking but can be affected by weather conditions. In contrast, vehicle-view datasets offer better temporal continuity and richer detail per frame but are more costly to record and sensitive to occlusion. Vehicle-view datasets often rely on sensor fusion, combining cameras for semantic understanding, LiDAR for precise 3D localization, and radar for long-range detection and velocity estimation [82].

Next Generation SIMulation (NGSIM) [80] is a publicly available dataset developed by the U.S. Federal Highway Administration in collaboration with academic and industry
partners. It captures traffic interactions over 500–640 meters of roadway using static cameras mounted on buildings across four locations: two freeways (US-101 and I-80) and two ar- terials (Peachtree Street and Lankershim Boulevard). NGSIM is widely used in maneuver-based prediction since it includes lane-level data for recognizing lateral and longitudinal maneu- vers.

Krajewski et al. [81] introduced the **HighD** dataset (highway drone dataset), a scenario-based highway dataset recorded in six locations near Cologne, Germany, using a 4 K drone-mounted camera covering 420 meters of road. Recordings were conducted under mild weather to ensure video quality. Vehicles were automatically annotated using a U-Net-based neural network, while static elements (e.g., lanes, signs) were labeled manually. A detailed comparison with NGSIM is available in [81].

KITTI [84], released in 2013, remains one of the first datasets designed for stereo, optical flow, 3D object detection, and tracking tasks. It was built using a VW Passat station wagon equipped with four cameras, one 3D laser scanner, and a GPS/IMU system. KITTI was not initially designed for TP tasks. However, some studies have adapted it for this purpose. For instance, Marchetti et al. [86] obtained 8613 top-view trajectories for training and 2907 for testing by sampling raw data into segments, each 6 seconds long (2 seconds of the past and 4 seconds of the future).

Argoverse (AV) remains among the most frequently used public datasets for vehicle TP. Its initial version, Argoverse 1 [71], released in 2019, comprises HD maps along with three datasets: the AV 1 3D tracking dataset, the AV 1 stereo dataset, and the AV 1 motion forecasting dataset. The three datasets were collected using a set of identical Ford Fusion Hybrids equipped with two LiDARs, seven-ring cameras, and two front-facing stereo cameras.

AV 1 motion prediction dataset is composed of 324,557 scenarios recorded in Miami and Pittsburgh. Each scenario is 5 seconds long. The provided maps are: i) the vector map that includes lane information like lane center lines, lane boundaries, and intersection annotations. And ii) two raster- ized maps, one containing the driveable area labels required to compute the DAC metric and the other for the ground height. AV 2 [83] is the upgraded version of AV 1, and it contains four datasets: i) a LiDAR dataset, ii) a sensor dataset for perception applications, iii) a map change dataset, and iii) a motion forecasting dataset providing 250,000 scenarios recorded in Miami, Austin, Washington DC, Pittsburgh, Palo Alto, and Detroit, each scenario is 11 seconds long.

Both datasets releases provide a Python API that allows users to explore, visualize, and evaluate their prediction method using the AV dataset. AV 1 API allows evalua- tion using the following metrics: minADE, minFDE, MR, p-minADE, p-minFDE, p-MR, brier-minADE, brier-minFDE, and DAC. Meanwhile, AV 2 API employs these metrics for evaluation: mean-ADE, mean-FDE, and mean mean Average Precision (mean-mAP) (a metric that combines both precision and recall and is generally used in object detection).

ApolloScape [73] is a public dataset and toolset devel- oped to advance autonomous driving, recorded in China using a mid-size SUV equipped with LiDAR, cameras, and IMU/GNSS. It includes resources for tasks like scene parsing, lane segmentation, localization, 3D LiDAR object detection and tracking, and TP. The TP dataset provides stereo images, LiDAR point clouds, and manually annotated GT trajectories. Like Argoverse, ApolloScape offers a Python API, including an evaluation script for TP that computes WADE and WFDE metrics.

Ettinger et al. [85] introduced **Waymo Open Motion Dataset (WOMD)**, designed for joint multi-agent trajectory prediction, as the position of up to 8 agents in the scene can be predicted, including vehicles, pedestrians, and cyclists. Collected across six U.S. cities using Waymo's autonomous fleet, the 2024 release contains 103,354 segments of 20 sec- onds each. It includes HD map data with lane boundaries and connections and supports standardized evaluation metrics such as minFDE, MR, overlap rate, and mAP.

NuScenes [82], developed by nuTonomy, is the first dataset to provide full 360-degree scene coverage using six cameras, five radars, one LiDAR, a GPU, and an IMU. It was recorded over 15 hours in Boston and Singapore using Renault Zoe vehicles. After sensor synchronization, an HD map was built offline using LiDAR data, along with manually annotated semantic maps covering 11 layers (e.g., lanes, traffic lights, drivable areas). From this data, 1,000 challenging 20-second scenes were selected.

Table 5 provides a comparison of VTP datasets in terms of environment, number of object classes, available infrastruc- ture data, used sensors, size, year of collection, and speed.

## C. CONCLUSION

This analysis aims to select the appropriate dataset, metrics, and prediction methods for benchmarking single-vehicle TP models on two hardware architectures: a high-performance architecture and an embedded architecture.

**Dataset selection:** to narrow the dataset selection, top- down-view datasets such as NGSIM and HighD are excluded, as this study focuses on vehicle-view datasets. Additionally, WOMD is excluded from this evaluation, since it is primar- ily designed for joint multi-agent TP. Among the remaining datasets (NuScenes, AV1 and 2, ApolloScape, and KITTI), only NuScenes offers multi-sensor data (camera, LiDAR, radar), but with limited driving time (5.5 h) compared to AV1 (320 h) and AV2 (763 h). ApolloScape includes only 103 minutes, while KITTI is rarely used for TP, being initially designed for stereo matching, optical flow, visual odometry, 3D object detection, and tracking.

Regarding data frequency, NuScenes and ApolloScape are sampled at 2 Hz, while AV 1 and 2 provide higher temporal resolution at 10 Hz. For real-time TP, a higher sampling rate is advantageous, as it allows the model to respond more ef- fectively to rapid changes in vehicle dynamics. Considering data frequency, dataset size, and available infrastructure data,
<table><caption>TABLE 5. Comparative Overview of Vehicle TP Datasets, Including Two Top-Down-View Datasets (NGSIM and HighD) and Six Vehicle-View Datasets, by Environment, Object Classes, Infrastructure Data Type, Sensor Configuration, Dataset Size, Frequency, Collection Year, and Recording Speed</caption><thead><tr><th>Data-set</th><th>Environment</th><th>Classes</th><th>Infrastructure</th><th>Sensors</th><th>Size</th><th>Frequency</th><th>Year</th><th>Speed</th></tr></thead><tbody><tr><td>NGSIM (US-101 and I-80) [80]</td><td>Freeways in Los Angeles and Emeryville, CA</td><td>3</td><td>Lane ID</td><td>Synchronized mounted cameras (7 for I-80, 8 for US-101)</td><td>45 minutes each for US-101 and I-80 (90 minutes total)</td><td>10 Hz</td><td>2005/ 2006</td><td>≈ 75 km/h</td></tr><tr><td>HighD [81]</td><td>German highways</td><td>2</td><td>Lane ID</td><td>Camera mounted on a drone</td><td>16.5 hrs</td><td>NA</td><td>2017/ 2018</td><td>≈ 160 km/h</td></tr><tr><td>NuScenes [82]</td><td>Dense urban scenarios in Singapore and Boston</td><td>23</td><td>HD map</td><td>6 cameras, 1 LiDAR, 5 radars, GPS, IMU</td><td>5,5 hrs</td><td>2 Hz</td><td>Released in 2019</td><td>≈ 16 km/h</td></tr><tr><td>AV 1 Motion Forecasting [71]</td><td>Urban scenarios in Miami and Pittsburgh</td><td>15</td><td>HD map</td><td>2 LiDARs, 7 ring cameras, and 2 front-facing stereo cameras</td><td>320 hrs</td><td>10 Hz</td><td>Released in 2019</td><td>NA</td></tr><tr><td>AV 2 Motion Forecasting [83]</td><td>Austin, Detroit, Miami, Pittsburgh, Palo Alto, and Washington</td><td>30</td><td>HD map</td><td>2 LiDARs, 7 ring cameras, and 2 front-facing stereo cameras</td><td>763 hrs</td><td>10 Hz</td><td>Description published in 2021</td><td>NA</td></tr><tr><td>ApolloScape trajectory data-set [73]</td><td>Urban streets in China</td><td>5</td><td>HD map</td><td>2 VUX-1HA LiDARs, 1 VMX-CS6 camera system, and IMU/GNSS unit</td><td>53 min of training scenarios and 50min testing</td><td>2 Hz</td><td>2019</td><td>≈ 30 km/h</td></tr><tr><td>KITTI [84]</td><td>Rural areas, highways, and city in Karlsruhe, Germany</td><td>3</td><td>NA</td><td>4 cameras, 3D rotating LiDAR, and GPS/IMU system</td><td>6 hrs</td><td>10–100 Hz</td><td>Recorder in 2011, released in 2013</td><td>NA</td></tr><tr><td>WOMD [85]</td><td>6 cities within the United States</td><td>4</td><td>3D HD maps</td><td>1 mid-range LiDAR 4 short-range LiDARs 8 cameras.</td><td>574 hrs</td><td>10 Hz</td><td>Release in 2021</td><td>NA</td></tr></tbody></table>

AV1 and AV2 are the most suitable datasets for evaluation. Although AV2 offers more scenes and richer metadata, its recent release was in 2021 compared to AV1, which was released in 2019, meaning that AV1 has seen broader adoption in the literature. Therefore, we selected AV1 for the experimental evaluation to benefit from more available baseline implementations.

**Metrics selection:** based on the selection of Argoverse 1 as the evaluation dataset, we adopt the trajectory prediction metrics provided by the Argoverse API: MR₁, MR₆, minADE₁, minADE₆, minFDE₁, minFDE₆, and DAC₁. These metrics capture both displacement-based metrics (minADE/minFDE) and ability-oriented metrics (MR and DAC). They are further complemented by model complexity and computational performance metrics outlined in the evaluation metrics section.

**TP methods selection:** to select SOTA prediction methods, we choose representative models from each category defined in Section IV (physics-based, lane-based, interaction-based, and maneuver-based), prioritizing those originally evaluated on the AV1 dataset. The AV1 dataset already provides several baseline methods, including the CV baseline. This baseline is the representative of the physics-based class, as well as additional implementations: CV with KF (KF-CV), CA, CA with KF (KF-CA), constant wheel angle and acceleration (CWAA), and CWAA with EKF (EKF-CWAA). These models collectively represent the class of physics-based prediction methods.

For the remaining categories, the selection of prediction methods is guided by both the performance and the

availability of open-source code. Within interaction-based methods, we distinguish between map-free and map-based approaches. As a representative of map-free models, we include CRAT-Pred [48], an encoder-decoder architecture that captures agents' interactions using a Graph Neural Network (GNN). It is distinguished by its low number of model parameters (514 K) while achieving comparable accuracy, making it a suitable candidate for deployment on embedded architectures. For map-based interaction-aware prediction, we select Autobots [51], which employs a transformer-based encoder-decoder architecture. A key contribution of this model is the use of seed parameters in the decoder, allowing it to generate the full future trajectory in a single forward pass rather than autoregressively. This design significantly reduces inference time, which is critical for real-time deployment. Furthermore, Autobots' architecture is well-suited for parallel execution on embedded architectures, as it is built on attention mechanisms.

We select LaneGCN [63] as a representative model in the category of lane-based trajectory prediction methods. LaneGCN is one of the earliest and most influential approaches to explicitly incorporate structured lane graph information into motion forecasting. Beyond its performance on the Argoverse 1 benchmark, the model's publicly available source code has made it highly accessible and reproducible since LaneGCN has served as a foundational architecture for several subsequent SOTA models, including [87] and [88].

Regarding maneuver-based methods, we note that most publicly available implementations are trained and tested on highway datasets such as NGSIM, where lane identification
is consistent (from 1 to 6) and aligned in a bird's-eye view
format. In contrast, AV 1, being an urban-driving dataset, uses
unique lane segment identifiers derived from its vectorized
map topology. This difference makes it challenging to adapt
maneuver classification methods from NGSIM to AV 1. To
address this, we evaluate Conv-Social-Pooling [53], initially
developed for NGSIM, on its native dataset and compare the
results despite dataset differences. Although NGSIM typically
uses a 5 s prediction horizon, the authors downsampled the ob-
servation and prediction lengths by 2, yielding a 2.5 s horizon
closer to the 3 s used in AV1. The selected learning-based
TP methods are implemented in Python using the PyTorch
framework. See Section IV for more details.

VI. EVALUATION RESULTS

In this section, we reproduce and evaluate the selected SOTA
single-vehicle TP methods using two hardware architectures.
Training and evaluation are first conducted on a high-
performance GPU architecture, after which the trained models
are deployed on an embedded architecture for inference-only
evaluation in order to assess the performance differences
when transitioning from high-performance to embedded envi-
ronments. According to Zhang et al. [24], the main hardware
platforms for embedded AI acceleration include FPGAs,
ASICs, GPUs, and NPUs. FPGAs (Field-Programmable Gate
Arrays) offer reconfigurable, energy-efficient solutions that
are ideal for adaptable AI problems. ASICs (Application-
Specific Integrated Circuits) provide high performance and
low power consumption but are inflexible, making them ideal
for fixed tasks and large-scale deployments. GPUs, while
powerful and widely supported for AI tasks, are typically
limited by high power consumption. Due to their compact
and integrated design, NPUs (Neural Processing Units) are
increasingly used in mobile and real-time applications.

In this study, evaluation is performed on two GPU-based
architectures. Despite GPUs' relatively high power consump-
tion, they offer key advantages in terms of programmability,
maturity of development tools, and support for parallel com-
putation. With proper optimization, training and inference on
GPUs can achieve power-efficient performance. The evalu-
ation is first conducted on a high-performance architecture
equipped with an NVIDIA RTX 6000 Ada Generation GPU,
which features 48 GB of GDDR6 ECC memory, 1457 TOPS
of AI performance, and 300 W of thermal design power.
The second evaluation is performed on an embedded GPU-
based architecture, NVIDIA Jetson, designed for low-power,
real-time AI applications. Fig. 3 benchmarks several Jetson
modules in terms of AI performance, power consumption, and
memory. In this work, the Jetson AGX Orin is used as it repre-
sents the highest-performance option within the Jetson family
and serves as a solid starting point for evaluating embedded
inference across different hardware configurations.

The AV1 dataset is divided into training, validation, and
test splits. It is worth noting that the training and validation
sets contain 5-second-long scenarios (2 s of observed history
and 3 s of future Ground-Truth (GT) positions), whereas the

<figure><img src="image_4.png" /><figcaption>**FIGURE 3.** Comparison of Jetson modules in terms of AI performance (TFLOPS or TOPS), power consumption (W), and memory capacity (GB). The bubble color indicates whether the reported AI performance is in TFLOPS or TOPS, while the bubble size reflects the memory size. Jetson AGX Orin offers the highest AI performance and memory capacity, but with a higher power consumption, while Jetson Nano provides an efficient low-power alternative with limited computing capabilities.</figcaption></figure>

test split contains only the observed history. Therefore, we
re-partitioned the training and validation data (which include
GT) into new training, validation, and test sets, following the
original ratio. The selected models were retrained on the new
training and validation sets and evaluated on the new test set
using the official AV1 metrics.

A. EVALUATION RESULTS ON A HIGH-PERFORMANCE
ARCHITECTURE

Performance metrics: since AV1 is selected as the primary
evaluation dataset, we adopt its official evaluation metrics:
MR<sub>k</sub>, minADE<sub>k</sub>, minFDE<sub>k</sub>, and DAC<sub>k</sub> for k ∈ 1, 6. Table 6
presents the performance of the three learning-based models:
AutoBots [51], CRAT-Pred [48], and LaneGCN [63], on the
AV1 using the selected metrics. Overall, LaneGCN demon-
strates superior performance on displacement-based metrics,
achieving a lower minFDE<sub>6</sub> by 0.019 m compared to Au-
toBots and 0.423 m compared to CRAT-Pred. Moreover, on
ability metrics, LaneGCN achieves the lowest MR<sub>6</sub> (11.54%),
outperforming AutoBots (12.48%) by 0.94 percentage points
(a relative increase of 8.15%), and CRAT-Pred (20.28%) by
8.74 percentage points (a relative increase of 75.74%). It also
performs slightly lower in terms of DAC<sub>6</sub> (98.91%) compared
to AutoBots (99.17%) by 0.26 percentage points, but notice-
ably higher than CRAT-Pred (96.76%) by 2.15 percentage
points.

Table 8 presents the evaluation results for physics-based
models. Since these methods generate a single determin-
istic trajectory, they are not compatible with multimodal
evaluation metrics such as minADE<sub>6</sub>, minFDE<sub>6</sub>, or DAC<sub>6</sub>.
Overall, simpler kinematic models like CV, KF-CV, and KF-
CA demonstrate relatively better performance. For instance,
the minFDE<sub>1</sub> of CWAA reaches 15.59 m, 8.74 m higher
than KF-CV (6.84 m), corresponding to a relative increase of
127.8%. This is primarily due to the reliance on complex state
variables (e.g., velocity, acceleration, heading, or curvature)
<table><caption>**TABLE 6.** Evaluation of Learning-Based Models on AV1 Using MR<sub>k</sub>, minADE<sub>k</sub>, minFDE<sub>k</sub>, and DAC<sub>k</sub> for k ∈ {1, 6}, on the High-Performance Architecture. LaneGCN Demonstrates Superior Overall Performance, Outperforming Both AutoBots and CRAT-Pred. Symbol (↓) is Used for Metrics That Should Be Minimized, and (↑) for Those That Should Be Maximized</caption><thead><tr><th>Reference</th><th>minADE<sub>6</sub> (↓)</th><th>minADE<sub>1</sub> (↓)</th><th>minFDE<sub>6</sub> (↓)</th><th>minFDE<sub>1</sub> (↓)</th><th>MR<sub>6</sub> (↓)</th><th>MR<sub>1</sub> (↓)</th><th>DAC<sub>6</sub> (↑)</th><th>DAC<sub>1</sub> (↑)</th></tr></thead><tbody><tr><td>AutoBots</td><td><strong>0.7892</strong></td><td>1.6511</td><td><strong>1.1897</strong></td><td>3.6716</td><td><strong>0.1248</strong></td><td>0.5694</td><td><strong>0.9917</strong></td><td><strong>0.9920</strong></td></tr><tr><td>CRAT-Pred</td><td>0.9343</td><td><strong>1.6157</strong></td><td>1.5940</td><td><strong>3.5746</strong></td><td>0.2028</td><td><strong>0.5612</strong></td><td>0.9676</td><td>0.9774</td></tr><tr><td>LaneGCN</td><td><strong>0.7658</strong></td><td><strong>1.5895</strong></td><td><strong>1.1707</strong></td><td><strong>3.5702</strong></td><td><strong>0.1154</strong></td><td><strong>0.5394</strong></td><td><strong>0.9891</strong></td><td><strong>0.9915</strong></td></tr></tbody></table>

<table><caption>**TABLE 7.** Evaluation of conv-Social-Pooling [53] Method on NGSIM Using MR<sub>k</sub>, minADE<sub>k</sub>, minFDE<sub>k</sub>, and DAC<sub>k</sub> for k ∈ {1, 6} on the High-Performance Architecture</caption><thead><tr><th>Reference</th><th>minADE<sub>6</sub> (↓)</th><th>minADE<sub>1</sub> (↓)</th><th>minFDE<sub>6</sub> (↓)</th><th>minFDE<sub>1</sub> (↓)</th><th>MR<sub>6</sub> (↓)</th><th>MR<sub>1</sub> (↓)</th><th>DAC<sub>6</sub> (↑)</th><th>DAC<sub>1</sub> (↑)</th></tr></thead><tbody><tr><td>CSP first trajectory (1)</td><td>1.93</td><td>2.61</td><td>4.22</td><td>6.76</td><td>0.47</td><td>0.65</td><td>-</td><td>-</td></tr><tr><td>CSP most likely trajectory (2)</td><td>1.93</td><td><strong>2.45</strong></td><td>4.22</td><td><strong>6.28</strong></td><td>0.47</td><td><strong>0.60</strong></td><td>-</td><td>-</td></tr></tbody></table>

<table><caption>**TABLE 8.** Evaluation of Physics-Based Models on AV1 Using MR<sub>1</sub>, minADE<sub>1</sub>, minFDE<sub>1</sub>, and DAC<sub>1</sub> on the High-Performance Architecture</caption><thead><tr><th>Reference</th><th>minADE<sub>1</sub> (↓)</th><th>minFDE<sub>1</sub> (↓)</th><th>MR<sub>1</sub> (↓)</th><th>DAC<sub>1</sub> (↑)</th></tr></thead><tbody><tr><td>CV</td><td><strong>3.0991</strong></td><td>6.9347</td><td>0.7727</td><td><strong>0.9216</strong></td></tr><tr><td>KF-CV</td><td>3.1038</td><td><strong>6.8414</strong></td><td><strong>0.7675</strong></td><td>0.9222</td></tr><tr><td>CA</td><td>4.1868</td><td>10.8424</td><td>0.8943</td><td>0.8215</td></tr><tr><td>KF-CA</td><td>3.1107</td><td>7.4188</td><td>0.8299</td><td>0.8803</td></tr><tr><td>CWAA</td><td>6.2177</td><td>15.5851</td><td>0.9842</td><td>0.7128</td></tr><tr><td>EKF-CWAA</td><td>3.686</td><td>7.806</td><td>0.8795</td><td>0.8252</td></tr></tbody></table>

that are not directly available in AV1, leading to accumulated estimation errors. However, even the best-performing physics-based model (KF-CV) remains significantly less accurate than learning-based models. For example, KF-CV reports a minFDE<sub>1</sub> of 6.84m, compared to just 3.57 m for CRAT-Pred, an absolute difference of 3.27 m, corresponding to a relative error increase of 91.35%.

Additionally, Table 7 reports the performance of Conv-Social-Pooling (CSP) [53] evaluated on the NGSIM dataset under two trajectory selection strategies when $k = 1$: (1) selecting the first predicted trajectory (as in the AV1 API), and (2) selecting the trajectory associated with the most probable maneuver. Although the latter yields improved performance on minADE<sub>1</sub>, minFDE<sub>1</sub>, and MR<sub>1</sub>, we adopt the first for consistency with the evaluation methodology used across the other methods. Note that DAC<sub>1</sub> and DAC<sub>6</sub> are not reported for CSP, as the NGSIM dataset does not provide drivable area maps. Moreover, CSP exhibits larger prediction errors compared to AV1-trained models. For instance, its minFDE<sub>6</sub> is 4.22 m, whereas CRAT-Pred achieves 3.57 m, an absolute difference of 0.65 m, representing a relative error increase of 18.21%. One possible reason for this performance gap is the limited data size of 90 minutes for NGSIM compared to AV1's 320 hours.

**Model complexity:** to assess model complexity, we evaluate AutoBots, CRAT-Pred, LaneGCN, and CSP using three metrics: total number of model parameters, model size in

MB, and FLOPs per forward pass in GFLOPs. FLOPs are computed for all models except LaneGCN (*), whose value is directly sourced from [29]. The results are summarized in Table 9. As expected, CSP is the least complex model, due to its lightweight LSTM-based encoder-decoder architecture with limited layer depth and width. It does not incorporate Graph Neural Networks (GNNs) or multi-head self-attention mechanisms like CRAT-Pred nor a vectorized map like AutoBots and LaneGCN, which are known to be computationally intensive in terms of both FLOPs and memory usage. AutoBots is approximately 2.9 times larger than CRAT-Pred in terms of parameter count. Its higher complexity stems from integrating vectorized map features with agent representations through stacked attention layers. Among the compared methods, LaneGCN is the most complex, with over 7 times more parameters than CRAT-Pred and 2.5 times more than AutoBots. It operates on a lane graph extracted from high-definition vectorized maps and includes a specialized FusionNet module that models four types of interactions: Actor-to-Lane (A2L), Lane-to-Lane (L2L), Lane-to-Actor (L2A), and Actor-to-Actor (A2A), resulting in greater model size and computational overhead.

Regarding parallelization potential, AutoBots is highly parallelizable as it is composed entirely of feedforward layers and multi-head attention blocks, which operate on independent input elements using optimized matrix operations. In contrast, CRAT-Pred employs LSTM encoders, which are inherently sequential and limit parallelism. However, its use of GNNs and attention layers introduces a degree of parallelizable computation, resulting in moderate parallelism. For LaneGCN, while it incorporates 1D CNNs and attention mechanisms, which support parallel execution, it also processes disconnected lane graphs using a multi-stage GNN pipeline, making the model only moderately parallelizable. Finally, CSP is dominated by LSTM encoder-decoder structures, which are fundamentally sequential. Although its convolutional social pooling layer offers some limited parallelism, the overall architecture remains poorly suited for parallel execution.
<table><caption>**TABLE 9.** Evaluation of AI Models on the High-Performance Architecture Based on the Number of Parameters, Model Size (MB), FLOPs Per Forward Pass (GFLOPs), and Map Dependency. CSP and CRAT-Pred are the Least Complex Models</caption><thead><tr><th>Reference</th><th>AI model</th><th>Parameters (↓)</th><th>Model size (↓)</th><th>Flops per sample (↓)</th><th>Map</th><th>Parallelization</th></tr></thead><tbody><tr><td>AutoBots</td><td>rFFN, Positional Encoding, MHA</td><td>1480966</td><td>5.65</td><td>0.279</td><td>Yes</td><td>+++</td></tr><tr><td>CRAT-Pred</td><td>LSTM, GNN and MHSA</td><td><b>514920</b></td><td><b>1.96</b></td><td><b>0.025</b></td><td>No</td><td>++</td></tr><tr><td>LaneGCN</td><td>1D-CNN, LaneGCN (GNN), Spatial Attention, FusionNet, MLP</td><td>3701161</td><td>14.12</td><td>0.13*</td><td>Yes</td><td>++</td></tr><tr><td>CSP (NGSIM)</td><td>LSTM, CSP, Maneuver-based Decoder</td><td><b>194954</b></td><td><b>0.74</b></td><td><b>0.001</b></td><td>Lane ID</td><td>+</td></tr></tbody></table>

<table><caption>**TABLE 10.** Evaluation of AI-Based Models on the High-Performance Architecture in Terms of Computational Performance, Including Inference Time, Average CPU–GPU and GPU–CPU Transfer Times, Average CPU Preprocessing Time for 5 s and 2 s Scenes, and Estimated End-to-End Processing Time (All in Milliseconds). The Lowest Total Processing Time is the One Required for CRAT-Pred</caption><thead><tr><th>Reference</th><th>Inference (↓)</th><th>CPU to GPU (↓)</th><th>GPU to CPU (↓)</th><th>CPU Preproc. (5s) (↓)</th><th>CPU Preproc. (2s) (↓)</th><th>Total (↓)</th></tr></thead><tbody><tr><td>AutoBots</td><td>3.7</td><td>0.14</td><td>0.12</td><td>49.3</td><td>19.72</td><td>23.62</td></tr><tr><td>CRAT-Pred</td><td><b>1.6</b></td><td>0.9</td><td>0.028</td><td>3.6</td><td><b>1.44</b></td><td><b>3.96</b></td></tr><tr><td>LaneGCN</td><td>4.851</td><td><b>0.049</b></td><td><b>0.017</b></td><td>32.7</td><td>13.08</td><td>17.99</td></tr><tr><td>CSP (NGSIM)</td><td>1.81</td><td>0.10</td><td>0.11</td><td>- (Matlab)</td><td>- (Matlab)</td><td>-</td></tr></tbody></table>

<table><caption>**TABLE 11.** Evaluation of the Selected AI-Based on AV1 Using MR<sub>k</sub>, minADE<sub>k</sub>, minFDE<sub>k</sub>, DAC<sub>k</sub> for k ∈ {1, 6} on the Embedded Architecture. Performance Remained Close Across Embedded and High-Performance Architectures</caption><thead><tr><th>Reference</th><th>minADE<sub>6</sub> (↓)</th><th>minADE<sub>1</sub> (↓)</th><th>minFDE<sub>6</sub> (↓)</th><th>minFDE<sub>1</sub> (↓)</th><th>MR<sub>6</sub> (↓)</th><th>MR<sub>1</sub> (↓)</th><th>DAC<sub>6</sub> (↑)</th><th>DAC<sub>1</sub> (↑)</th></tr></thead><tbody><tr><td>AutoBots</td><td><b>0.7892</b></td><td>1.6512</td><td><b>1.1897</b></td><td>3.6719</td><td><b>0.1248</b></td><td>0.5693</td><td><b>0.9917</b></td><td><b>0.9921</b></td></tr><tr><td>CRAT-Pred</td><td>0.9357</td><td><b>1.6174</b></td><td>1.5958</td><td><b>3.5788</b></td><td>0.2029</td><td><b>0.5629</b></td><td>0.9676</td><td>0.9775</td></tr></tbody></table>

<table><caption>**TABLE 12.** Evaluation of AI-Based Models on the Embedded Architecture in Terms of Computational Performance, Including Inference Time, Average CPU–GPU and GPU–CPU Transfer Times, Average CPU Preprocessing Time for 5 s and 2 s Scenes, and Estimated End-to-End Processing Time (All in Milliseconds). CRAT-Pred is Approximately 4.9× Faster Than Autobots</caption><thead><tr><th>Reference</th><th>Inference (↓)</th><th>CPU to GPU (↓)</th><th>GPU to CPU (↓)</th><th>Preprocessing (5s) (↓)</th><th>Preprocessing (2s) (↓)</th><th>Total(↓)</th></tr></thead><tbody><tr><td>AutoBots</td><td>25.1</td><td><b>1.34</b></td><td>1.27</td><td>178.08</td><td>71.23</td><td>98.94</td></tr><tr><td>CRAT-Pred</td><td><b>12.5</b></td><td>1.97</td><td><b>0.697</b></td><td><b>12.3</b></td><td><b>4.92</b></td><td><b>20.08</b></td></tr></tbody></table>

*Computational performance:* to evaluate computational performance, we standardized the batch size to 1, reflecting real-time embedded application scenarios in which models typically process a single input sample at a time. All inference operations for AI-based models were executed on the GPU, and the following performance metrics (in milliseconds) were recorded: average preprocessing time, average CPU-to-GPU data transfer time, average inference time on the GPU, average GPU-to-CPU data transfer time, and total processing time. Preprocessing is applied over the full 5-second scene duration (2 s past + 3 s future). However, for estimating total processing time, only the portion corresponding to the 2-second past observation window is considered in the average preprocessing time. To better reflect realistic in-vehicle deployment, the processing was configured to operate sequentially. Note that the preprocessing time for the CSP method is not reported due to its implementation in MATLAB, which is incompatible with the Python-based timing framework used for the other models. Additionally, all evaluations were conducted within Docker containers to ensure consistency across environments. Table 10 summarizes the computational performance of

AutoBots, CRAT-Pred, LaneGCN, and CSP. All these models demonstrate low average GPU inference times, typically below 5 ms. However, AutoBots and LaneGCN have significant preprocessing time, which must also be executed in real time for online deployment. When considering the full processing time, CRAT-Pred demonstrates the lowest total processing time of 3.96 ms, compared to 23.62 ms for AutoBots and 17.99 ms for LaneGCN, making it approximately 6× faster than AutoBots and 4.5× faster than LaneGCN. Thus, in high-performance architecture, learning-based models meet the previously defined real-time execution requirements within the 20–30 ms threshold.

Table 13 reports the CPU-based prediction time and total processing time for physics-based methods. The total processing time includes an estimated preprocessing duration for a 2-second observation window. Since preprocessing operations are shared across all physics-based models, the preprocessing time for a 5-second scene is 2.33 ms, yielding 0.93 ms for a 2-second scene. Because these models do not rely on deep learning components and are not designed for GPU acceleration, evaluating them on GPU hardware would not provide
<table><caption>TABLE 13. Evaluation of Physics-Based Models on the High-Performance Architecture in Terms of CPU Prediction Time and Estimated End-to-End Processing Time (Prediction + 2 s Preprocessing Time of 0.93 ms), All in Ms</caption><thead><tr><td>Reference</td><td>Processing time on CPU (↓)</td><td>Total (↓)</td></tr></thead><tbody><tr><td>CV</td><td>0.08</td><td>1.01</td></tr><tr><td>KF-CV</td><td>0.62</td><td>1.55</td></tr><tr><td>CA</td><td>0.16</td><td>1.09</td></tr><tr><td>KF-CA</td><td>0.67</td><td>1.6</td></tr><tr><td>CWAA</td><td>0.12</td><td>1.05</td></tr><tr><td>EKF-CWAA</td><td>0.96</td><td>1.89</td></tr></tbody></table>

meaningful insights. As expected, physics-based methods exhibit significantly lower computational overhead. In terms of total processing time, the CV model is approximately 3.9 times faster than the learning-based CRAT-Pred, reflecting the algorithmic simplicity of these approaches.

## B. EVALUATION RESULTS ON AN EMBEDDED ARCHITECTURE

The evaluation on the NVIDIA RTX 6000 is conducted to train the models and to identify which prediction approaches, among those explored on the NVIDIA RTX 6000, would be selected for inference-only evaluation on the embedded architecture. Physics-based methods are inherently suitable for embedded systems due to their low complexity and minimal processing time; however, they exhibit lower accuracy, particularly in terms of final displacement error $minFDE_1$, rendering them insufficient for long-term predictions. Consequently, we focus on AI-based approaches, which offer higher predictive accuracy. Among these, LaneGCN, AutoBots, and CRAT-Pred demonstrate superior performance, while CSP is less accurate and difficult to evaluate computationally due to the lack of detailed processing time information. In terms of model complexity, CRAT-Pred is the least complex, followed by AutoBots and then LaneGCN, based on FLOPs, model size, and parameter count. Given that LaneGCN has the largest model size and number of parameters, CRAT-Pred and AutoBots are selected for embedded evaluation due to their favorable trade-offs between predictive accuracy and computational feasibility.

Table 11 presents the displacement and ability metrics results for AutoBots and CRAT-Pred on the NVIDIA Jetson AGX Orin, while Table 12 summarizes their computational performance on the same embedded architecture. The algorithmic accuracy of both models remains consistent when transitioning from the high-performance NVIDIA RTX 6000 GPU to the embedded Jetson architecture. However, computational performance revealed a significant change. The full processing time for AutoBots increased from 23.62 ms on the RTX 6000 to 98.94 ms on Jetson AGX Orin, an increase of 75.32 ms, making it approximately 4.2× slower. Similarly, CRAT-Pred's total processing time increased from 3.96 ms to 20.08 ms, a 16.12 ms increase corresponding to a 5.1× slowdown. Despite this, CRAT-Pred still meets the real-time

execution requirement defined in the 20–30 ms range, even on Jetson AGX Orin. However, achieving similar performance on more resource-constrained architectures such as the NVIDIA Jetson Nano may require additional optimization. In contrast, AutoBots, with a total processing time nearing 100 ms, exceeds the real-time threshold, highlighting the need for a joint hardware-software optimization strategy to meet real-time constraints.

## VII. CONCLUSION AND FUTURE DIRECTIONS

In this study, we first classified and reviewed SOTA vehicle TP methods based on a new proposed taxonomy. We then evaluated publicly available models trained on the AV 1 dataset across two distinct GPU architectures: a high-performance NVIDIA RTX 6000 Ada Generation GPU and an embedded-oriented NVIDIA Jetson AGX Orin. The evaluation was conducted using algorithmic performance metrics, as well as model complexity and computational performance, under conditions closely resembling real-time applications. The results highlight the gap in computational performance when transitioning from high-performance to embedded architectures. This gap is explained by key hardware differences: the RTX 6000 offers far more CUDA cores, higher compute throughput (TFLOPS), and greater memory and CPU resources than the Jetson AGX Orin, enabling faster preprocessing and inference time. However, while the Jetson AGX Orin offers significantly higher performance than typical in-vehicle embedded systems, it also incurs higher energy consumption. This highlights the importance of deploying and evaluating prediction models on lower-power embedded architectures and exploring optimization strategies to reduce processing time, ensuring that TP remains accurate and compliant with real-time constraints in energy-limited environments. CRAT-Pred [48] is a strong candidate to explore its performance on different embedded architectures due to its fast inference and preprocessing time on the NVIDIA Jetson AGX Orin. Its low preprocessing and inference time are largely due to its map-free design, which minimizes preprocessing, and its lightweight and parallelizable model architecture. Additionally, its ability to operate without requiring map data, makes it well-suited for deployment in vehicles that lack access to high-definition maps. The model's architecture also presents opportunities for further optimization to enhance inference efficiency on embedded hardware, either by redesigning the architecture or exploring other methods like pruning or quantization.

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<figure><img src="image_6.png" /></figure>

**OUMAIMA SKALLI** received the Engineering degree (equivalent to an M.S. degree) in electromechanics from ENSAM, Meknès, Morocco, in 2022, and the M.S. degree in computer science-Industry 4.0 from the Université de Pau et des Pays de l'Adour, Pau, France, in 2023. She is currently working toward the Ph.D. degree with Université Paris-Saclay, Gif-sur-Yvette, France. Her research interests include road scene analysis, perception for advanced driver assistance systems, and algorithm-architecture mapping.
<figure><img src="image_7.png" /></figure>

**SERGIO RODRIGUEZ FLOREZ** received the M.S. and Ph.D. degrees from the University of Technology of Compiègne, Compiègne, France, in 2007 and 2010, respectively. Since 2011, he has been an Associate Professor with the University Paris-Saclay, Gif-sur-Yvette, France. He is currently dedicated to developing context-aware, AI-based embedded multimodal perception systems. These systems are designed to significantly enhance intelligent vehicle applications, aiming to improve safety and efficiency. His research focuses on

embedded multimodal perception systems for dynamic scene analysis, specifically within the research community of intelligent transportation systems. He was the recipient of the accreditation to supervise research (HDR) in 2023 based on his research contributions to embedded multimodal perception systems for dynamic scene analysis applied to intelligent transportation systems.

<figure><img src="image_8.png" /></figure>

**ABDELHAFID EL OUARDI** received the M.S. degree from Pierre & Marie Curie University, Paris, France, in 2001, and the Ph.D. degree in electronics from Paris-Sud University, France, in 2005. In 2006, he was a Temporary Teaching and Research Assistant with Henri Poincaré University, Nancy, France. He is currently an Associate Professor with Paris-Saclay University, Gif-sur-Yvette, France. He is also with Embedded Systems Team, SATIE Laboratory. His research interests include algorithm-architecture mapping, design of smart

architectures for image, and signal real-time processing. He was the recipient of the accreditation to supervise Research (HDR) in 2016.

<figure><img src="image_9.png" /></figure>

**STEFANO MASI** (Associate Member, IEEE) received the B.S. and M.S. degrees in computer engineering from the Università degli Studi di Genova (UNIGE), Genoa, Italy, in 2015 and 2017, respectively, and the Master Diploma and the Ph.D. degree in robotics and artificial intelligence from the Université de Technologie de Compiègne (UTC), Compiègne, France, in 2017 and 2021, respectively. During this time, he also contributed as a Research and Development Engineer to the French National Project Tornado. From 2021 to

2023, he was a Research and Development Engineer with Navya Group, where he focused on perception, object tracking, and multi-sensor data fusion for obstacle avoidance in autonomous shuttles. Since 2023, he has been an ADAS Engineer with Ampère Software Technologies, contributing to the development of key ADAS features, such as Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), and Lane Keeping. His research interests include autonomous vehicle navigation, collision avoidance, sensor fusion, and environment perception, with a strong focus on applications in self-driving vehicles and ADAS systems.