Search Results for « Electric Vehicles »

Deep learning has been widely used in the perception (e.g., 3D object detection) of intelligent vehicle driving. Due to the beneficial Vehicle-to-Vehicle (V2V) communication, the deep learning based features from other agents can be shared to the ego vehicle so as to improve the perception of the ego vehicle. It is named as Cooperative Perception in the V2V research, whose algorithms have been dramatically advanced recently. However, all the existing cooperative perception algorithms assume the ideal V2V communication without considering the possible lossy shared features because of the Lossy Communication (LC) which is common in the complex real-world driving scenarios. In this paper, we first study the side effect (e.g., detection performance drop) by the lossy communication in the V2V Cooperative Perception, and then we propose a novel intermediate LC-aware feature fusion method to relieve the side effect of lossy communication by a LC-aware Repair Network (LCRN) and enhance the interaction between the ego vehicle and other vehicles by a specially designed V2V Attention Module (V2VAM) including intra-vehicle attention of ego vehicle and uncertainty-aware inter-vehicle attention. The extensive experiment on the public cooperative perception dataset OPV2V (based on digital-twin CARLA simulator) demonstrates that the proposed method is quite effective for the cooperative point cloud based 3D object detection under lossy V2V communication.

The introduction of Smart Electric Vehicles (SEVs) represents an increasingly disruption on automotive area, once integrates advanced computer and communication technologies to highly electrical cars, which come with high performances, environment friendly and user friendly characteristics . But the increasing complexity of SEVs prompted by greater dependence on interconnected systems, autonomous capabilities and electrification, presents new challenges in cybersecurity as well as functional safety. The safety and reliability of such vehicles is paramount, as unsafe or unreliable operation in either case represents an unacceptable risk in terms of the performance of the vehicle and safety of the passenger. This paper investigates the integrated development of cybersecurity and functional safety for SEVs, emphasizing the requirement for the parallel development of these domains as components that are not treated separately. In SEVs, cybersecurity is quite crucial in order to prevent the threats of hacking, data breaches and unauthorized access to vehicle systems. Functional safety ensures that important vehicle functions (braking, steering, battery control, etc.) keep working even if some part fails. This convergence of functional safety issues with cybersecurity issues is becoming more crucial, since a security incident can result in a failure of catastrophic consequences for a functional safety system and, conversely. This paper reports the current state of cybersecurity and functional safety standards for SEVs, highlighting challenges that include the weaknesses of communication networks, the potential security threats of over-the-air updates, and the demand for real-time responsive systems for failure.

Reliable prediction of surround vehicle motion is a critical requirement for path planning for autonomous vehicles. In this paper we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and inter-vehicle interaction. We report our results in terms of maneuver classification accuracy and mean and median absolute error of predicted trajectories against the ground truth for real traffic data collected using vehicle mounted sensors on freeways. An ablative analysis is performed to analyze the relative importance of each cue for trajectory prediction. Additionally, an analysis of execution time for the components of the framework is presented. Finally, we present multiple case studies analyzing the outputs of our model for complex traffic scenarios

Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles.

For the offline safety assessment of automated vehicles, the most challenging and critical scenarios must be identified efficiently. Therefore, we present a new approach to define challenging scenarios based on a sensor setup model of the ego-vehicle. First, a static optimal approaching path of a road user to the ego-vehicle is calculated using an A* algorithm. We consider a poor perception of the road user by the automated vehicle as optimal, because we want to define scenarios that are as critical as possible. The path is then transferred to a dynamic scenario, where the trajectory of the road user and the road layout are determined. The result is an optimal road geometry, so that the ego-vehicle can perceive an approaching object as poorly as possible. The focus of our work is on the highway as the Operational Design Domain (ODD).