Current research on automotive perception systems predominantly focusses on either improving the sensors for data quality or enhancing the performance of perception functions in isolation. Although automotive perception sensors form a fundamental part of the perception system, value addition in sensor data quality in isolation is questionable. However, the end goal for most perception systems is the accuracy of high-level functions such as trajectory prediction of surrounding vehicles. High-level perception functions are increasingly based on deep learning (DL) models due to their improved performance and generalisability compared to traditional algorithms. Innately, DL models develop a performance bias on the comprehensiveness of the training data. Despite the vital need to evaluate the performance of DL-based perception functions under real-world conditions using onboard sensor inputs, there is a lack of frameworks to facilitate systematic evaluations. This paper presents a versatile and cost-effective framework to evaluate the impact of perception sensor modalities and parameter settings on DL-based perception functions. Using a simulation environment, the framework facilitates sensor modality testing and parameter tuning under different environmental conditions. Its effectiveness is demonstrated through a case study involving a state-of-the-art surround trajectory prediction model, highlighting performance differences across sensor modalities and recommending optimal parameter settings. The proposed framework offers valuable insights for designing the perception sensor suite, contributing to the development of robust perception systems for autonomous vehicles.

In the rapidly advancing landscape of connected and automated vehicles (CAV), the integration of Vehicle-to-Everything (V2X) communication in traditional fusion systems presents a promising avenue for enhancing vehicle perception. Addressing current limitations with vehicle sensing, this paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system that leverages the synergy between V2V signals and detections from radar and camera sensors. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. This approach enables more flexible and resilient thresholds for track management, particularly in scenarios with numerous occlusions where the tracked objects move outside the field of view of the perception sensors. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors. Presented are the fusion algorithm, simulated environments, and validation mechanisms. Experimental results demonstrate the improved accuracy and robustness of the proposed system in common driving scenarios, highlighting its potential to advance the reliability and efficiency of autonomous vehicles.

Smart roads have become an essential component of intelligent transportation systems (ITS). The roadside perception technology, a critical aspect of smart roads, utilizes various sensors, roadside units (RSUs), and edge computing devices to gather real-time traffic data for vehicle-road cooperation. However, the full potential of smart roads in improving the safety and efficiency of autonomous vehicles only can be realized through the mass deployment of roadside perception and communication devices. On the one hand, roadside devices require significant investment but can only achieve monitoring function currently, resulting in no profitability for investors. On the other hand, drivers lack trust in the safety of autonomous driving technology, making it difficult to promote large-scale commercial applications. To deal with the dilemma of mass deployment, we propose a novel smart-road vehicle-guiding architecture for vehicle-road cooperative autonomous driving, based on which we then propose the corresponding business model and analyze its benefits from both operator and driver perspectives. The numerical simulations validate that our proposed smart road solution can enhance driving safety and traffic efficiency. Moreover, we utilize the cost-benefit analysis (CBA) model to assess the economic advantages of the proposed business model which indicates that the smart highway that can provide vehicle-guided-driving services for autonomous vehicles yields more profit than the regular highway.

As a rapidly growing cyber-physical platform, Autonomous Vehicles (AVs) are encountering more security challenges as their capabilities continue to expand. In recent years, adversaries are actively targeting the perception sensors of autonomous vehicles with sophisticated attacks that are not easily detected by the vehicles' control systems. This work proposes an Anomaly Behavior Analysis approach to detect a perception sensor attack against an autonomous vehicle. The framework relies on temporal features extracted from a physics-based autonomous vehicle behavior model to capture the normal behavior of vehicular perception in autonomous driving. By employing a combination of model-based techniques and machine learning algorithms, the proposed framework distinguishes between normal and abnormal vehicular perception behavior. To demonstrate the application of the framework in practice, we performed a depth camera attack experiment on an autonomous vehicle testbed and generated an extensive dataset. We validated the effectiveness of the proposed framework using this real-world data and released the dataset for public access. To our knowledge, this dataset is the first of its kind and will serve as a valuable resource for the research community in evaluating their intrusion detection techniques effectively.

The vehicle's perception sensors radar, lidar and camera, which must work continuously and without restriction, especially with regard to automated/autonomous driving, can lose performance due to unfavourable weather conditions. This paper analyzes the sensor signals of these three sensor technologies under rain and fog as well as day and night. A data set of a driving test vehicle as an object target under different weather conditions was recorded in a controlled environment with adjustable, defined, and reproducible weather conditions. Based on the sensor performance evaluation, a method has been developed to detect sensor degradation, including determining the affected data areas and estimating how severe they are. Through this sensor monitoring, measures can be taken in subsequent algorithms to reduce the influences or to take them into account in safety and assistance systems to avoid malfunctions.

New technology is giving autonomous vehicles'X-ray' vision to help them track pedestrians, cyclists and other vehicles that may be obscured. Experts in Australia are now commercialising the technology, which is called cooperative or collective perception (CP). It involves the installation of roadside information-sharing units ('ITS stations') equipped with sensors such as cameras and lidar. At a busy junction, for example, vehicles would use these units to share what they'see' with other vehicles. This gives each vehicle X-ray style vision that sees through buses to notice pedestrians, or a fast-moving van around a corner that's about to run a red light.