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In this paper, we study the problem of traffic signal control in general intersections by applying a recent reinforcement learning technique. Nowadays, traffic congestion and road usage are increasing significantly as more and more vehicles enter the same infrastructures. New solutions are needed to minimize travel times or maximize the network capacity (throughput). Recent studies embrace machine learning approaches that have the power to aid and optimize the increasing demands. However, most reinforcement learning algorithms fail to be adaptive regarding goal functions. To this end, we provide a novel successor feature-based solution to control a single intersection to optimize the traffic flow, reduce the environmental impact, and promote sustainability. Our method allows for flexibility and adaptability to changing circumstances and goals. It supports changes in preferences during inference, so the behavior of the trained agent (traffic signal controller) can be changed rapidly during the inference time. By introducing the successor features to the domain, we define the basics of successor features, the base reward functions, and the goal preferences of the traffic signal control system. As our main direction, we tackle environmental impact reduction and support prioritized vehicles' commutes. We include an evaluation of how our method achieves a more effective operation considering the environmental impact and how adaptive it is compared to a general Deep-Q-Network solution. Aside from this, standard rule-based and adaptive signal-controlling technologies are compared to our method to show its advances. Furthermore, we perform an ablation analysis on the adaptivity of the agent and demonstrate a consistent level of performance under similar circumstances. [ABSTRACT FROM AUTHOR]

Planning the optimal trajectory of emergency avoidance maneuvers for highly automated vehicles is a complex task with many challenges. The algorithm needs to decrease accident risk by reducing the severity and keeping the car in a controllable state. Optimal trajectory generation considering all aspects of vehicle and environment dynamics is numerically complex, especially if the object to be avoided is moving. This paper presents a hierarchical method for the avoidance of moving objects in an autonomous vehicle, where a reinforcement learning agent is responsible for local planning, while longitudinal and lateral control is performed by the low-level model-predictive controller and Stanley controllers. In the developed architecture, the agent is responsible for the optimization. It is trained in various scenarios to provide the necessary parameters for a polynomial-based path and a velocity profile in a neural network output. The vehicle performs only the first step of the trajectory, which is redesigned repeatedly by the planner based on the new state. In the training phase, the vehicle executes the entire trajectory via low-level controllers to determine the reward value, which realizes a prediction for the future. The agent receives feedback and can further improve its performance. Finally, the proposed framework was tested in a simulation environment and was also compared to human drivers' abilities. [ABSTRACT FROM AUTHOR]

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA. [ABSTRACT FROM AUTHOR]

It is essential for a driver assistant system's motion planning to take the vehicles moving in the surroundings into account. One of the most crucial driver intentions which should be predicted is lane changing. It has been investigated whether it is possible to reliably classify lane-changing maneuvers in a highway situation using learning algorithms such as Gaussian-classifier, SVM, and LSTM neural networks. Real vehicle trajectories are extracted from the NGSIM US-101 and I-80 datasets. The input for the classifiers is derived from the trajectory by selecting a subset of the features: lateral and longitudinal position coordinates, longitudinal acceleration, and velocity. In such an environment, the vehicle movement is limited, so it has been tested that how sufficient if only the mean and the variance of the derivative of lateral coordinate was taken as input for the classification had been tested. Different strategies for labeling the input sequences were tested. [ABSTRACT FROM AUTHOR]

Reinforcement Learning, as one of the main approaches of machine learning, has been gaining high popularity in recent years, which also affects the vehicle industry and research focusing on automated driving. However, these techniques, due to their self-training approach, have high computational resource requirements. Their development can be separated into training with simulation, validation through vehicle dynamics software, and real-world tests. However, ensuring portability of the designed algorithms between these levels is difficult. A case study is also given to provide better insight into the development process, in which an online trajectory planner is trained and evaluated in both vehicle simulation and real-world environments. [ABSTRACT FROM AUTHOR]