Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions

Nowadays, transportation networks face the challenge of sub-optimal control policies that can have adverse effects on human health, the environment, and contribute to traffic congestion. Increased levels of air pollution and extended commute times caused by traffic bottlenecks make intersection traffic signal controllers a crucial component of modern transportation infrastructure. Despite several adaptive traffic signal controllers in literature, limited research has been conducted on their comparative performance. Furthermore, despite carbon dioxide (CO2) emissions' significance as a global issue, the literature has paid limited attention to this area. In this report, we propose EcoLight, a reward shaping scheme for reinforcement learning algorithms that not only reduces CO2 emissions but also achieves competitive results in metrics such as travel time. We compare the performance of tabular Q-Learning, DQN, SARSA, and A2C algorithms using metrics such as travel time, CO2 emissions, waiting time, and stopped time. Our evaluation considers multiple scenarios that encompass a range of road users (trucks, buses, cars) with varying pollution levels.

Fuel Consumption Prediction for a Passenger Ferry using Machine Learning and In-service Data: A Comparative Study

As the importance of eco-friendly transportation increases, providing an efficient approach for marine vessel operation is essential. Methods for status monitoring with consideration to the weather condition and forecasting with the use of in-service data from ships requires accurate and complete models for predicting the energy efficiency of a ship. The models need to effectively process all the operational data in real-time. This paper presents models that can predict fuel consumption using in-service data collected from a passenger ship. Statistical and domain-knowledge methods were used to select the proper input variables for the models. These methods prevent over-fitting, missing data, and multicollinearity while providing practical applicability. Prediction models that were investigated include multiple linear regression (MLR), decision tree approach (DT), an artificial neural network (ANN), and ensemble methods. The best predictive performance was from a model developed using the XGboost technique which is a boosting ensemble approach. \rvv{Our code is available on GitHub at \url{https://github.com/pagand/model_optimze_vessel/tree/OE} for future research.

LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning

In end-to-end autonomous driving, the utilization of existing sensor fusion techniques for imitation learning proves inadequate in challenging situations that involve numerous dynamic agents. To address this issue, we introduce LeTFuser, a transformer-based algorithm for fusing multiple RGB-D camera representations. To perform perception and control tasks simultaneously, we utilize multi-task learning. Our model comprises of two modules, the first being the perception module that is responsible for encoding the observation data obtained from the RGB-D cameras. It carries out tasks such as semantic segmentation, semantic depth cloud mapping (SDC), and traffic light state recognition. Our approach employs the Convolutional vision Transformer (CvT) \cite{wu2021cvt} to better extract and fuse features from multiple RGB cameras due to local and global feature extraction capability of convolution and transformer modules, respectively. Following this, the control module undertakes the decoding of the encoded characteristics together with supplementary data, comprising a rough simulator for static and dynamic environments, as well as various measurements, in order to anticipate the waypoints associated with a latent feature space. We use two methods to process these outputs and generate the vehicular controls (e.g. steering, throttle, and brake) levels. The first method uses a PID algorithm to follow the waypoints on the fly, whereas the second one directly predicts the control policy using the measurement features and environmental state. We evaluate the model and conduct a comparative analysis with recent models on the CARLA simulator using various scenarios, ranging from normal to adversarial conditions, to simulate real-world scenarios. Our code is available at \url{https://github.com/pagand/e2etransfuser/tree/cvpr-w} to facilitate future studies.

Task-Oriented Koopman-Based Control with Contrastive Encoder

We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator and associated linear controller within an iterative loop. By prioritizing the task cost as main objective for controller learning, we reduce the reliance of controller design on a well-identified model, which extends Koopman control beyond low-dimensional systems to high-dimensional, complex nonlinear systems, including pixel-based scenarios.

An MCTS-DRL Based Obstacle and Occlusion Avoidance Methodology in Robotic Follow-Ahead Applications

We propose a novel methodology for robotic follow-ahead applications that address the critical challenge of obstacle and occlusion avoidance. Our approach effectively navigates the robot while ensuring avoidance of collisions and occlusions caused by surrounding objects. To achieve this, we developed a high-level decision-making algorithm that generates short-term navigational goals for the mobile robot. Monte Carlo Tree Search is integrated with a Deep Reinforcement Learning method to enhance the performance of the decision-making process and generate more reliable navigational goals. Through extensive experimentation and analysis, we demonstrate the effectiveness and superiority of our proposed approach in comparison to the existing follow-ahead human-following robotic methods. Our code is available at https://github.com/saharLeisiazar/follow-ahead-ros.

Hierarchical Planning and Policy Shaping Shared Autonomy for Articulated Robots

In this work, we propose a novel shared autonomy framework to operate articulated robots. We provide strategies to design both the task-oriented hierarchical planning and policy shaping algorithms for efficient human-robot interactions in context-aware operation of articulated robots. Our framework for interplay between the human and the autonomy, as the participating agents in the system, is particularly influenced by the ideas from multi-agent systems, game theory, and theory of mind for a sliding level of autonomy. We formulate the sequential hierarchical human-in-the-loop decision making process by extending MDPs and Options framework to shared autonomy, and make use of deep RL techniques to train an uncertainty-aware shared autonomy policy. To fine-tune the formulation to a human, we use history of the system states, human actions, and their error with respect to a surrogate optimal model to encode human's internal state embeddings, beyond the designed values, by using conditional VAEs. We showcase the effectiveness of our formulation for different human skill levels and degrees of cooperativeness by using a case study of a feller-buncher machine in the challenging tasks of timber harvesting. Our framework is successful in providing a sliding level of autonomy from fully autonomous to fully manual, and is particularly successful in handling a noisy non-cooperative human agent in the loop. The proposed framework advances the state-of-the-art in shared autonomy for operating articulated robots, but can also be applied to other domains where autonomous operation is the ultimate goal.

Efficient Domain Coverage for Vehicles with Second Order Dynamics via Multi-Agent Reinforcement Learning

Collaborative autonomous multi-agent systems covering a specified area have many potential applications, such as UAV search and rescue, forest fire fighting, and real-time high-resolution monitoring. Traditional approaches for such coverage problems involve designing a model-based control policy based on sensor data. However, designing model-based controllers is challenging, and the state-of-the-art classical control policy still exhibits a large degree of suboptimality. In this paper, we present a reinforcement learning (RL) approach for the multi-agent coverage problem involving agents with second-order dynamics. Our approach is based on the Multi-Agent Proximal Policy Optimization Algorithm (MAPPO). To improve the stability of the learning-based policy and efficiency of exploration, we utilize an imitation loss based on the state-of-the-art classical control policy. Our trained policy significantly outperforms the state-of-the-art. Our proposed network architecture includes incorporation of self attention, which allows a single-shot domain transfer of the trained policy to a large variety of domain shapes and number of agents. We demonstrate our proposed method in a variety of simulated experiments.

DMODE: Differential Monocular Object Distance Estimation Module without Class Specific Information

Using a single camera to estimate the distances of objects reduces costs compared to stereo-vision and LiDAR. Although monocular distance estimation has been studied in the literature, previous methods mostly rely on knowing an object's class in some way. This can result in deteriorated performance for dataset with multi-class objects and objects with an undefined class. In this paper, we aim to overcome the potential downsides of class-specific approaches, and provide an alternative technique called DMODE that does not require any information relating to its class. Using differential approaches, we combine the changes in an object's size over time together with the camera's motion to estimate the object's distance. Since DMODE is class agnostic method, it is easily adaptable to new environments. Therefore, it is able to maintain performance across different object detectors, and be easily adapted to new object classes. We tested our model across different scenarios of training and testing on the KITTI MOTS dataset's ground-truth bounding box annotations, and bounding box outputs of TrackRCNN and EagerMOT. The instantaneous change of bounding box sizes and camera position are then used to obtain an object's position in 3D without measuring its detection source or class properties. Our results show that we are able to outperform traditional alternatives methods e.g. IPM \cite{TuohyIPM}, SVR \cite{svr}, and \cite{zhu2019learning} in test environments with multi-class object distance detections.

Online Probabilistic Model Identification using Adaptive Recursive MCMC

The Bayesian paradigm provides a rigorous framework for estimating the whole probability distribution over unknown parameters, but due to high computational costs, its online application can be difficult. We propose the Adaptive Recursive Markov Chain Monte Carlo (ARMCMC) method, which calculates the complete probability density function of model parameters while alleviating the drawbacks of traditional online methods. These flaws include being limited to Gaussian noise, being solely applicable to linear in the parameters (LIP) systems, and having persisting excitation requirements (PE). A variable jump distribution based on a temporal forgetting factor (TFF) is proposed in ARMCMC. The TFF can be utilized in many dynamical systems as an effective way to adaptively present the forgetting factor instead of a constant hyperparameter. The particular jump distribution has tailored towards hybrid/multi-modal systems that enables inferences among modes by providing a trade-off between exploitation and exploration. These trade-off are adjusted based on parameter evolution rate. In comparison to traditional MCMC techniques, we show that ARMCMC requires fewer samples to obtain the same accuracy and reliability. We show our method on two challenging benchmarks: parameter estimation in a soft bending actuator and the Hunt-Crossley dynamic model. We also compare our method with recursive least squares and the particle filter, and show that our technique has significantly more accurate point estimates as well as a decrease in tracking error of the value of interest.

STPOTR: Simultaneous Human Trajectory and Pose Prediction Using a Non-Autoregressive Transformer for Robot Following Ahead

In this paper, we develop a neural network model to predict future human motion from an observed human motion history. We propose a non-autoregressive transformer architecture to leverage its parallel nature for easier training and fast, accurate predictions at test time. The proposed architecture divides human motion prediction into two parts: 1) the human trajectory, which is the hip joint 3D position over time and 2) the human pose which is the all other joints 3D positions over time with respect to a fixed hip joint. We propose to make the two predictions simultaneously, as the shared representation can improve the model performance. Therefore, the model consists of two sets of encoders and decoders. First, a multi-head attention module applied to encoder outputs improves human trajectory. Second, another multi-head self-attention module applied to encoder outputs concatenated with decoder outputs facilitates learning of temporal dependencies. Our model is well-suited for robotic applications in terms of test accuracy and speed, and compares favorably with respect to state-of-the-art methods. We demonstrate the real-world applicability of our work via the Robot Follow-Ahead task, a challenging yet practical case study for our proposed model.