ACT: Perceptive Acting Under Uncertainty

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People

Khaled Mustafa - PhD candidate
Anna Meszaros - PhD candidate
Daniel Jarne - Postdoctoral researcher
Ahmad Gazar - Postdoctoral researcher
Prof. Jens Kober
Prof. Javier Alonso-Mora

Funding

This project is funded the the Dutch Research Council NWO-NWA, within the "Acting under Uncertainty (ACT)" project (Grant No. NWA.1292.19.298).

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About the Project

A successful integration of autonomous robots in the real-world depends on them taking socially acceptable actions. The ACT project bridges Neuroscience, Behavioral Psychology, Robotics, and AI to study interactions with humans and autonomous systems and develop new application for safe navigation. Our Lab’s role in the project is to create a fundamental understanding of how autonomous agents can cope with uncertainty and demonstrate risk-aware autonomous agents that are demonstrably trustable and predictable.

This project contributed towards improving the safety of autonomous vehicles(AVs) by introducing a method to learn human-like driving behaviours which are then encoded into a local planner. Such behaviors are desired in AVs so that human drivers can trust these systems and also enables smoother coordination with other agents on the road. This behavior is learnt through an interactive imitation learning approach which is data efficient and is then encoded into a model predictive contouring controller. The proposed method is validated using a simulator which demonstrated it’s ability to learn human-like driving behaviors.

Another contribution from this project has been to predict human behavior in traffic. This is an important and challenging aspect since often times human behavior is not deterministic. Previous research has often struggled with random uncertainties in human movements which are difficult to model. A novel model called TrajFlow is presented which is capable of fitting distributions present in training samples. A recurrent neural network autoencoder is developed which captures the relevant features of a trajectory and the decoder is able to predict trajectories beyond the length of the training data.

Project Demonstrations

Funding & Partners

This project is funded the the Dutch Research Council NWO-NWA, within the “Acting under Uncertainty (ACT)” project (Grant No. NWA.1292.19.298).

TU Delft partners

ROME: Robust Multi-Modal Density Estimator
Anna Mészáros, Julian F Schumann, Javier Alonso-Mora, Arkady Zgonnikov, Jens Kober. In 33rd International Joint Conference on Artificial Intelligence (IJCAI), 2024.

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The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demonstrated to lack robustness, while more complex methods have not been evaluated in multi-modal estimation problems. In this paper, we present ROME (RObust Multi-modal Estimator), a non-parametric approach for density estimation which addresses the challenge of estimating multi-modal, non-normal, and highly correlated distributions. ROME utilizes clustering to segment a multi-modal set of samples into multiple uni-modal ones and then combines simple KDE estimates obtained for individual clusters in a single multi-modal estimate. We compared our approach to state-of-the-art methods for density estimation as well as ablations of ROME, showing that it not only outperforms established methods but is also more robust to a variety of distributions. Our results demonstrate that ROME can overcome the issues of over-fitting and over-smoothing exhibited by other estimators.

TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction
A. Meszaros, J. F. Schumann, J. Alonso-Mora, A. Zgonnikov, J. Kober. In IEEE Intelligent Vehicles Symposium (IV), 2024.

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Predicting the future behavior of human road users is an important aspect for the development of risk-aware autonomous vehicles. While many models have been developed towards this end, effectively capturing and predicting the variability inherent to human behavior still remains an open challenge. This paper proposes TrajFlow—a new approach for probabilistic trajectory prediction based on Normalizing Flows. We reformulate the problem of capturing distributions over trajectories into capturing distributions over abstracted trajectory features using an autoencoder, simplifying the learning task of the Normalizing Flows. TrajFlow outperforms state-of-the-art behavior prediction models in capturing full trajectory distributions in two synthetic benchmarks with known true distributions, and is competitive on the naturalistic datasets ETH/UCY, rounD, and nuScenes. Our results demonstrate the effectiveness of TrajFlow in probabilistic prediction of human behavior.

RACP: Risk-Aware Contingency Planning with Multi-Modal Predictions
Khaled A. Mustafa, Daniel Jarne Ornia, Jens Kober, Javier Alonso-Mora. In IEEE Transactions on Intelliegent Vehicles (T-IV), 2024.

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For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is imperative to assess the repercussions of its prospective actions by anticipating the uncertain intentions exhibited by other participants in the traffic environment. Driven by the pronounced multi-modal nature of human driving behavior, this paper presents an approach that leverages Bayesian beliefs over the distribution of potential policies of other road users to construct a novel risk-aware probabilistic motion planning framework. In particular, we propose a novel contingency planner that outputs long-term contingent plans conditioned on multiple possible intents for other actors in the traffic scene. The Bayesian belief is incorporated into the optimization cost function to influence the behavior of the short-term plan based on the likelihood of other agents' policies. Furthermore, a probabilistic risk metric is employed to fine-tune the balance between efficiency and robustness. Through a series of closed-loop safety-critical simulated traffic scenarios shared with human-driven vehicles, we demonstrate the practical efficacy of our proposed approach that can handle multi-vehicle scenarios.

Probabilistic Risk Assessment for Chance-Constrained Collision Avoidance in Uncertain Dynamic Environments
Khaled A. Mustafa, Oscar de Groot, Xinwei Wang, Jens Kober, Javier Alonso-Mora. In IEEE International Conference on Robotics and Automation (ICRA), 2023.

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Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging where it is imperative to accomplish the robot’s mission without incurring any safety violations. Typically, chance constraints are incorporated into the planning problem to provide probabilistic safety guarantees by imposing an upper bound on the collision probability of the planned trajectory. Yet, this results in overly conservative behavior on the grounds that the gap between the obtained risk and the specified upper limit is not explicitly restricted. To address this issue, we propose a real-time capable approach to quantify the risk associated with planned trajec- tories obtained from multiple probabilistic planners, running in parallel, with different upper bounds of the acceptable risk level. Based on the evaluated risk, the least conservative plan is selected provided that its associated risk is below a specified threshold. In such a way, the proposed approach provides probabilistic safety guarantees by attaining a closer bound to the specified risk, while being applicable to generic uncertainties of moving obstacles. We demonstrate the efficiency of our proposed approach, by improving the performance of a state- of-the-art probabilistic planner, in simulations and experiments using a mobile robot in an environment shared with humans.

TrajFlow: Learning the Distribution over Trajectories
A. Meszaros, J. Alonso-Mora, J. Kober. In 5th Workshop on Long-term Human Motion Prediction at IEEE Int. Conf. on Robotics and Automation (ICRA), 2023.

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Predicting the future behaviour of people re- mains an open challenge for the development of risk-aware autonomous vehicles. An important aspect of this challenge is effectively capturing the uncertainty inherent to human behaviour. This paper studies an approach for multi-modal probabilistic motion forecasting of an agent with improved accuracy in the predicted sample likelihoods. Our approach achieves state-of-the-art results on the inD dataset when evalu- ated with the standard metrics employed for motion forecasting. Furthermore, our approach also achieves state-of-the-art results when evaluated with respect to the likelihoods it assigns to its generated trajectories. Evaluations on artificial datasets indicate that the distributions learned by our model closely correspond to the true distributions observed in data and are not as prone to being over-confident in a single outcome in the face of uncertainty.

Visually-Guided Motion Planning for Autonomous Driving from Interactive Demonstrations
R. Perez-Dattari, B. Brito, O. de Groot, J. Kober, J. Alonso-Mora. In IFAC Engineering Applications of Artificial Intelligence Journal, 2022.

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The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban scenarios these approaches can become costly and suboptimal. In this paper, we introduce a motion planning framework consisting of two components: a data-driven policy that uses visual inputs and human feedback to generate socially compliant driving behaviors (encoded by high-level decision variables), and a local trajectory optimization method that executes these behaviors (ensuring safety). In particular, we employ Interactive Imitation Learning to jointly train the policy with the local planner, a Model Predictive Controller (MPC), which results in safe and human-like driving behaviors. Our approach is validated in realistic simulated urban scenarios. Qualitative results show the similarity of the learned behaviors with human driving. Furthermore, navigation performance is substantially improved in terms of safety, i.e., number of collisions, as compared to prior trajectory optimization frameworks, and in terms of data-efficiency as compared to prior learning-based frameworks, broadening the operational domain of MPC to more realistic autonomous driving scenarios.

Learning Interaction-Aware Guidance for Trajectory Optimization in Dense Traffic Scenarios
B. Brito, A. Agarwal, J. Alonso-Mora. In IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2022.

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Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors. To maneuver through dense traffic, AVs must be able to reason how their actions affect others (interaction model) and exploit this reasoning to navigate through dense traffic safely. This paper presents a novel framework for interaction-aware motion planning in dense traffic scenarios. We explore the connection between human driving behavior and their velocity changes when interacting. Hence, we propose to learn, via deep Reinforcement Learning (RL), an interaction-aware policy providing global guidance about the cooperativeness of other vehicles to an optimization-based planner ensuring safety and kinematic feasibility through constraint satisfaction. The learned policy can reason and guide the local optimization-based planner with interactive behavior to pro-actively merge in dense traffic while remaining safe in case the other vehicles do not yield. We present qualitative and quantitative results in highly interactive simulation environments (highway merging and unprotected left turns) against two baseline approaches, a learning-based and an optimization-based method. The presented results demonstrate that our method significantly reduces the number of collisions and increases the success rate with respect to both learning-based and optimization-based baselines.