Browsing by Author "Cecotti, Marco"
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Item Open Access Autonomous navigation for mobility scooters: a complete framework based on open-source software(IEEE, 2019-11-28) Cecotti, Marco; Kanchwala, Husain; Aouf, NabilIn recent years, there has been a growing demand for small vehicles targeted at users with mobility restrictions and designed to operate on pedestrian areas. The users of these vehicles are generally required to be in control for the entire duration of their journey, but a lot more people could benefit from them if some of the driving tasks could be automated. In this scenario, we set out to develop an autonomous mobility scooter, with the aim to understand the commercial feasibility of a similar product. This paper reports on the progress of this project, proposing a framework for autonomous navigation on pedestrian areas, and focusing in particular on the construction of suitable costmaps. The proposed framework is based on open-source software, including a library created by the authors for the generation of costmaps.Item Open Access CAVE GDP 2022 Photos(Cranfield University, 2022-07-01 14:54) Cecotti, MarcoJust some pictures of the Group Design Project for the MSc in Connected and Autonomous Vehicle EngineeringItem Open Access CogShift - Typical Trial(Cranfield University, 2023-12-11 15:47) Cecotti, MarcoThis is the video of one of the vehicle trials for the CogShift project. CogShift, one of five projects which are part of an £11 million UK Government investment in autonomous vehicle research, studied driver attention and cognitive control characteristics. The project developed an optimal control-authority shifting system which takes driver attention into account. More information can be found at https://www.cranfield.ac.uk/research-projects/cogshift.Item Open Access Dataset "Results of centreline extraction based on maximal disks"(Cranfield University, 2025-03-03) Cecotti, Marco; Yin, ChenhuiMaps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centrelines are valuable references in the prediction and planning of trajectories, most centreline extraction methods only achieve satisfactory accuracy with high computational cost and limited performance in sparsely described scenarios. This paper explores the problem of centreline extraction based on a sparse set of border points, evaluating the performance of different approaches on both a self-created and a public dataset, and proposing a novel method to extract the lane centreline by searching and linking the internal maximal circles along the lane. Compared with other centreline extraction methods producing similar numbers of centre points, the proposed approach is significantly more accurate: in our experiments, based on a self-created dataset of road layouts, it achieves a max deviation below 0.15 m and an overall RMSE less than 0.01 m, against the respective values of 1.7 m and 0.35 m for a popular approach based on Voronoi tessellation, and 1 m and 0.25 m for an alternative approach based on distance transform.Item Open Access Deep‐learning‐based vehicle trajectory prediction: a review(Institution of Engineering and Technology (IET), 2025-01-01) Yin, Chenhui; Cecotti, Marco; Auger, Daniel J.; Fotouhi, Abbas; Jiang, HaobinVehicle trajectory prediction enables autonomous vehicles to better reason about fast‐changing driving scenarios and thus perform well‐informed decision‐making tasks. Among different prediction approaches, deep learning‐based (DL‐based) methodologies stand out because of their capabilities to efficiently summarise historical data, infer nonlinear behavioural patterns from human driving data, and perform long‐horizon prediction. This work reviews the DL‐based methods that have shown promising results, organising them in terms of usage of the input data, separating the encodings of the target vehicle's historical data, surrounding vehicle's historical data, and road layout data. In particular, this paper explores the relationships between the scope of the prediction components and the input data formats, as well as the connections with other elements in the same prediction framework, including vehicle interaction and road scene mining. This information is crucial to understand complex architectural decisions and to provide guidance for the design of improved solutions. This work also compares the performance of the most successful prediction models, establishing that appropriate encodings of vehicle interactions and road scenes improve trajectory prediction accuracy, with the best performance achieved by attention mechanism and Transformer‐based models. Finally, this work discusses future research directions, including considerations for real‐time applications.Item Open Access Lane centerline extraction based on surveyed boundaries: an efficient approach using maximal disks(MDPI, 2025-04-18) Yin, Chenhui; Cecotti, Marco; Auger, Daniel J.; Fotouhi, Abbas; Jiang, HaobinMaps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centerlines are valuable references in the prediction and planning of trajectories, most centerline extraction methods only achieve satisfactory accuracy with high computational cost and limited performance in sparsely described scenarios. This paper explores the problem of centerline extraction based on a sparse set of border points, evaluating the performance of different approaches on both a self-created and a public dataset, and proposing a novel method to extract the lane centerline by searching and linking the internal maximal circles along the lane. Compared with other centerline extraction methods producing similar numbers of center points, the proposed approach is significantly more accurate: in our experiments, based on a self-created dataset of road layouts, it achieves a max deviation below 0.15 m and an overall RMSE less than 0.01 m, against the respective values of 1.7 m and 0.35 m for a popular approach based on Voronoi tessellation, and 1 m and 0.25 m for an alternative approach based on distance transform.Item Open Access NDT RC: Normal Distribution Transform Occupancy 3D Mapping with recentering(IEEE, 2023-02-28) Courtois, Hugo; Aouf, Nabil; Ahiska, Kenan; Cecotti, MarcoThe Normal Distribution Transform Occupancy Map (NDT OM) is a mapping algorithm able to represent a dynamic 3D environment. The resulting map has fixed boundaries, thus a robot with unbounded displacement might fall outside of the map due to memory limitation. In this paper, a recentering algorithm called NDT RC is proposed to avoid this issue. NDT RC extends the use of NDT OM for vehicles with unbounded displacements. NDT RC provides a seamless translation of the map as the robot gets far from the center of the previous map. The influence of NDT RC on the precision of the estimated trajectory of the robot, or odometry, is examined on two publicly available datasets, the KITTI and Ford datasets. An analysis of the sensitivity of the NDT RC to its tuning parameters is carried out using the Ford dataset, while the KITTI dataset is used to measure the influence of the density of the input point cloud. The results show that the proposed recentering strategy improves the accuracy of the odometry calculated by registering the latest lidar scan on the generated map compared to other NDT based approaches (NDT OM, NDT OM Fusion, SE-NDT). In particular, the proposed method, which does not perform loop closure, reduces the mean absolute translation error by 16% and the runtime by 88% compared to the NDT OM Fusion on the Ford dataset.Item Open Access OAST: Obstacle Avoidance System for Teleoperation of UAVs(IEEE, 2022-01-27) Courtois, Hugo; Aouf, Nabil; Ahiska, Kenan; Cecotti, MarcoThis article presents a novel flight assistance system, obstacle avoidance system for teleoperation (OAST), whose main role is to make teleoperation of small multirotor unmanned aerial vehicles (UAVs) safer and more efficient in closed spaces. The OAST allows the operator to avoid obstacles while keeping a liberty of movement. The UAV is controlled through a 3-D haptic controller and the OAST amends the user input to increase safety and efficiency of the teleoperation. The design of the OAST is verified in computerized experiments. Moreover, a simulation involving 20 participants is carried out to validate the proposed scheme. This experiment shows that the OAST improves the completion time of the scenarios by 41% on average while reducing the workload of the operator from 57 to 27 points on the NASA Task Load Index test. The number of collisions with the environment is all but eliminated in these scenarios.Item Open Access Optimal control of race car with aerodynamic slipstreaming effect(IEEE, 2024-05-13) Liu, Xuze; Fotouhi, Abbas; Cecotti, Marco; Auger, DanielThis article presents a new method to describe the aerodynamics slipstreaming effect on the downstream car. This new approach can be implemented in lap time simulations (LTSs) and used to study the optimal trajectory of a downstream car operating in the wake of an upstream car. Two different scenarios are investigated using this method. In the energy-saving scenario for electric racing cars, the result shows the optimal strategy varies depending on the upstream car’s pace and the initial gap between the two cars. Chasing to stay in the wake is less effective when the initial gap is relatively big. In the overtaking scenario on an oval track, it is shown that the wake of the upstream car benefits the downstream car’s acceleration but, meanwhile, reduces the lateral performance limit of the downstream car due to downforce loss. In order to maintain a competitive performance, it is essential for the downstream car to choose an alternative racing line to drive outside the wake when braking and passing through a corner.Item Open Access Results of Centreline Extraction Based on Maximal Disks(Cranfield University, 2023-06-07 11:28) Yin, Chenhui; Cecotti, Marco; Auger, Daniel; Fotouhi, AbbasResults of centreline extraction based on maximal disks in a chosen lanelet2 map.