Vehicle 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.

This 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.

Electric Vehicles (EVs) present several characteristics which can greatly enhance the performance of the vehicle stability control. The torque response is fast and precise, the wheel speed can be easily estimated and the small dimensions of the motors allow mounting of multiple motors on the same vehicle, each one driving a single wheel. Taking advantage of such characteristics will allow a standard motor controller to integrate feature such as traction-control system (TCS), anti-lock braking system (ABS) and direct yaw control (DYC). This paper presents a novel technique to estimate both the slip ratio and the road friction characteristic without any hardware outside a standard motor controller. Such technique is unaffected by external forces acting on the vehicle. It works by adding a small oscillation to the set torque and then measuring the gain and the phase shift of the wheel speed oscillation. These values can be correlated to the transfer function obtained linearising the vehicle model around an operating point, which is function of the slip ratio and the road characteristics. A SIMULINK simulation is used to validate the method.

The 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.

This 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.