Examinando por Autor "Paniego, Sergio"

Mostrando 1 - 4 de 4

Autonomous driving in traffic with end-to-end vision-based deep learning
(Elsevier, 2024-08-14) Paniego, Sergio; Shinohara, Enrique; Cañas, José María
This paper presents a shallow end-to-end vision-based deep learning approach for autonomous vehicle driving in traffic scenarios. The primary objectives include lane keeping and maintaining a safe distance from preceding vehicles. This study leverages an imitation learning approach, creating a supervised dataset for robot control from expert agent demonstrations using the state-of-the-art Carla simulator in different traffic conditions. This dataset encompasses three different versions complementary to each other and we have made it publicly available along with the rest of the materials. The PilotNet neural model is utilized in two variants: the first one with complementary outputs for brake and throttle control commands along with dropout; the second one incorporates these improvements and adds the vehicle speed. Both models have been trained with the aforementioned dataset. The experimental results demonstrate that the models, despite their simplicity and shallow architecture, including only small-scale changes, successfully drive in traffic conditions without sacrificing performance in free-road environments, broadening their area of application widely. Additionally, the second model adeptly maintains a safe distance from leading cars and exhibits satisfactory generalization capabilities to diverse vehicle types. A new evaluation metric to measure the distance to the front vehicle has been created and added to Behavior Metrics; an open-source autonomous driving assessment tool built on CARLA that performs experimental validations of autonomous driving solutions
Behavior metrics: An open-source assessment tool for autonomous driving tasks
(Elsevier, 2024-05) Paniego, Sergio; Calvo-Palomino, Roberto; Cañas, José María
The development and validation of autonomous driving solutions require testing broadly in simulation. Addressing this requirement, we present Behavior Metrics (BM) for the quantitative and qualitative assessment and comparison of solutions for the main autonomous driving tasks. This software provides two evaluation pipelines, one with a graphical user interface used for qualitative assessment and the other headless for massive and unattended tests and benchmarks. It generates a series of quantitative metrics complementary to the simulator’s, including fine-grained metrics for each particular driving task (lane following, driving in traffic, route navigation, etc.). It provides a deeper and broader understanding of the solutions’ performance and allows their comparison and improvement. It uses and supports state-of-the-art open software such as the reference CARLA simulator, the ROS robotics middleware, PyTorch, and TensorFlow deep learning frameworks. BehaviorMetrics is available open-source for the community
Enhancing end-to-end control in autonomous driving through kinematic-infused and visual memory imitation learning
(Elsevier, 2024) Paniego, Sergio; Calvo-Palomino, Roberto; Cañas, José María
This paper presents an exploration, study, and comparison of various alternatives to enhance the capabilities of an end-to-end control system for autonomous driving based on imitation learning by adding visual memory and kinematic input data to the deep learning architectures that govern the vehicle. The experimental comparison relies on fundamental error metrics (MAE, MSE) during the offline assessment, supplemented by several external complementary fine-grain metrics based on the behavior of the ego vehicle at several urban test scenarios in the CARLA reference simulator in the online evaluation. Our study focuses on a lane-following application using different urban scenario layouts and visual bird-eye-view input. The memory addition involves architectural modifications and different sensory input types. The kinematic data integration is managed with a modified input. The experiments encompass both typical driving scenarios and extreme never-seen conditions. Additionally, we conduct an ablation study examining various memory lengths and densities. We prove experimentally that incorporating visual memory capabilities and kinematic input data makes the driving system more robust and able to handle a wider range of challenging situations, including those not encountered during training, in terms of reduction of collisions and speed self-regulation, resulting in a 75% enhancement. All the work we present here, including model architectures, trained model weights, comparison tool, and the dataset, is open-source, facilitating replication and extension of our findings
Enhancing end-to-end control in autonomous driving through kinematic-infused and visual memory imitation learning Author links open overlay panel
(Elsevier, 2024) Paniego, Sergio; Calvo-Palomino, Roberto; Cañas, JoséMaría
This paper presents an exploration, study, and comparison of various alternatives to enhance the capabilities of an end-to-end control system for autonomous driving based on imitation learning by adding visual memory and kinematic input data to the deep learning architectures that govern the vehicle. The experimental comparison relies on fundamental error metrics (MAE, MSE) during the offline assessment, supplemented by several external complementary fine-grain metrics based on the behavior of the ego vehicle at several urban test scenarios in the CARLA reference simulator in the online evaluation. Our study focuses on a lane-following application using different urban scenario layouts and visual bird-eye-view input. The memory addition involves architectural modifications and different sensory input types. The kinematic data integration is managed with a modified input. The experiments encompass both typical driving scenarios and extreme never-seen conditions. Additionally, we conduct an ablation study examining various memory lengths and densities. We prove experimentally that incorporating visual memory capabilities and kinematic input data makes the driving system more robust and able to handle a wider range of challenging situations, including those not encountered during training, in terms of reduction of collisions and speed self-regulation, resulting in a 75% enhancement. All the work we present here, including model architectures, trained model weights, comparison tool, and the dataset, is open-source, facilitating replication and extension of our findings.