MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.Comment: Code: https://github.com/westny/mtp-g

Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

Given their adaptability and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex ones, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun's can significantly improve predictions.Comment: https://github.com/westny/mtp-g

Stability-Informed Initialization of Neural Ordinary Differential Equations

This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques. It is shown how the choice of integration technique implicitly regularizes the learned model, and how the solver's corresponding stability region affects training and prediction performance. From this analysis, a stability-informed parameter initialization technique is introduced. The effectiveness of the initialization method is displayed across several learning benchmarks and industrial applications

Data-Driven Interaction-Aware Behavior Prediction for Autonomous Vehicles

Future progress toward the realization of fully self-driving vehicles still re-quires human-level social compliance, arguably dependent on the ability to accurately forecast the behavior of surrounding road users. Due to the inter-connected nature of traffic participants, in which the actions of one agent can significantly influence the decisions of others, the development of behavior pre-diction methods is crucial for achieving resilient autonomous motion planning. As high-quality data sets become more widely available and many vehicles already possess significant computing power, the possibility of adopting a data-driven approach for motion prediction is increasing.  The first contribution is the design of an intention-prediction model based on autoencoders for highway scenarios. Specifically, the method targets the problem of data imbalance in highway traffic data using ensemble methods and data-sampling techniques. The study shows that commonly disregarded information holds potential use for improved prediction performance and the importance of dealing with the data imbalance problem.  The second contribution is the development of a probabilistic motion pre-diction framework. The framework is used to evaluate various graph neural network architectures for multi-agent prediction across various traffic scenarios. The graph neural network computes the inputs to an underlying motion model, parameterized using neural ordinary differential equations. The method additionally introduces a novel uncertainty propagation approach by combining Gaussian mixture modeling and extended Kalman filtering techniques.  The third contribution is attributed to the investigation of combing data-driven models with motion modeling and methods for numerical integration. The study illustrates that improved prediction performance can be achieved by the inclusion of differential constraints in the model, but that the choice of motion model as well as numerical solver can have a large impact on the prediction performance. It is also shown that the added differential constraints improve extrapolation properties compared to complete black-box approaches.  The thesis illustrates the potential of data-driven methods and their usability for the behavior prediction problem. Still, there are additional challenges and interesting questions to investigate—the main one being the investigation of their use in autonomous navigation frameworks. Funding agencies: This research was supported by the Strategic Research Area at Linköping-Lund in Information Technology (ELLIIT), and the Wallenberg AI, Autonomous Sys-tems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.</p

Modellering och lateral reglering av lastbilsfordon under aggressiva manövrar

In the last decades, the development of self-driving vehicles has rapidly increased. Improvements in algorithms, as well as sensor and computing hardware have led to self-driving technologies becoming a reality. It is a technology with the potential to radically change how society interacts with transportation. One crucial part of a self-driving vehicle is control schemes that can safely control the vehicle during evasive maneuvers. This work investigates the modeling and lateral control of tractor-trailer vehicles during aggressive maneuvers. Models of various complexity are used, ranging from simple kinematic models to complex dynamic models, which model tire slip and suspension dynamics. The models are evaluated in simulations using TruckMaker, which is a high fidelity vehicle simulator. Several lateral controllers are proposed based on Model predictive control (MPC) and linear-quadratic (LQ) control techniques. The controllers use different complex prediction models and are designed to minimize the path-following error with respect to a geometric reference path. Their performance is evaluated on double lane change maneuvers of various lengths and with different longitudinal speeds. Additionally, the controllers' robustness against changes in trailer mass, weight distribution, and road traction is investigated. Extensive simulations show that dynamic prediction models are necessary to keep the control errors small when performing maneuvers that result in large lateral accelerations. Furthermore, to safely control the tractor-trailer vehicle during high speeds, it is a necessity to include a model of the trailer dynamics. The simulation study also shows that the proposed LQ controllers have trouble to evenly balance tractor and trailer deviation from the path, while the MPC controllers handle it much better. Additionally, a method for approximately weighting the trailer deviation is shown to improve the performance of both the LQ and MPC controllers. Finally, it is concluded that an MPC controller with a dynamic tractor-trailer model is robust against model errors, and can become even more robust by tuning the controller weights conservatively

Uncertainties in Robust Planning and Control of Autonomous Tractor-Trailer Vehicles

To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed. The implications of uncertainties in the model are then quantified and presented using sensitivity analysis and closed-loop simulations. The study shows that different model parameters are more or less critical depending on the investigated scenario. Using sampling-based closed-loop predictions, uncertainty bounds on state variable trajectories are determined. Our findings suggest the potential for the inclusion of our method within a robust predictive controller or as a driver-assistance system for rollover or lane departure warning

Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques

The use of learning-based methods for vehicle behavior prediction is a promising research topic. However, many publicly available data sets suffer from class distribution skews which limits learning performance if not addressed. This paper proposes an interaction-aware prediction model consisting of an LSTM autoencoder and SVM classifier. Additionally, an imbalanced learning technique, the multiclass balancing ensemble is proposed. Evaluations show that the method enhances model performance, resulting in improved classification accuracy. Good generalization properties of learned models are important and therefore a generalization study is done where models are evaluated on unseen traffic data with dissimilar traffic behavior stemming from different road configurations. This is realized by using two distinct highway traffic recordings, the publicly available NGSIM US-101 and I80 data sets. Moreover, methods for encoding structural and static features into the learning process for improved generalization are evaluated. The resulting methods show substantial improvements in classification as well as generalization performance

MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics

Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun's can greatly improve predictions