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The emergence of new autonomous driving systems and functions – in particular, systems that base their decisions on the output of machine learning subsystems responsible for environment perception – brings a significant change in the risks to the safety and security of transportation. These kinds of Advanced Driver Assistance Systems are vulnerable to new types of malicious attacks, and their properties are often not well understood. This paper demonstrates the theoretical and practical possibility of deliberate physical adversarial attacks against deep learning perception systems in general, with a focus on safety-critical driver assistance applications such as traffic sign classification in particular. Our newly developed traffic sign stickers are different from other similar methods insofar that they require no special knowledge or precision in their creation and deployment, thus they present a realistic and severe threat to traffic safety and security. In this paper we preemptively point out the dangers and easily exploitable weaknesses that current and future systems are bound to face.

Autonomous driving poses numerous challenging problems, one of which is perceiving and understanding the environment. Since self-driving is safety critical and many actions taken during driving rely on the outcome of various perception algorithms (for instance all traffic participants and infrastructural objects in the vehicle's surroundings must reliably be recognized and localized), thus the perception might be considered as one of the most critical subsystems in an autonomous vehicle. Although the perception itself might further be decomposed into various sub-problems, such as object detection, lane detection, traffic sign detection, environment modeling, etc. In this paper the focus is on fusion models in general (giving support for multisensory data processing) and some related automotive applications such as object detection, traffic sign recognition, end-to-end driving models and an example of taking decisions in multi-criterial traffic situations that are complex for both human drivers and for the self-driving vehicles as well.

We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud.

Activity Chain Optimization (ACO) is the task of finding a minimum-cost tour that visits exactly one location for each required activity while respecting time window constraints. We develop an exac...

A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

This article presents two prospective methods for vehicle distance estimation utilizing a single camera at different altitudes and camera pitch angles. A distance estimation procedure was conducted applying real-life visualization and homography approaches. Experimental outputs showed that the real-life visualization method overperforms the homography at all altitudes and distance ranges. The performance of the homography can be improved by locating the camera at higher locations with more pronounced tilt angles as it would provide less error / pixel at the cost of a reduced field of view. Overall, both introduced techniques perform better at higher camera positions and closer target distances. Application of mentioned methods in vehicles is a simple and economical way to improve their autonomy and reduce road risk level.

Autonomous driving functions increasingly incorporate machine learning methods for perception, environment modeling and decision making. The ever-present danger of unintended behavior of neural networks has given cause for concern in safety-critical applications and has spurred research towards the establishment of certified properties. In our work we present a general approach for exploring guaranteed properties of neural networks through a specific use case. We first methodically specified several desired properties for an ideal traffic sign classifier. Then we applied a mixed integer linear programming verification method to certify (or conversely, falsify) that our pre-trained rectified linear unit (ReLU) activation network will satisfy given properties under all conditions. We established through relaxations of our initial specification that our model does in fact happen to guarantee certain (but not all) properties at a different threshold. We thus showed that it is possible to establish the degree to which non-trivial and humanly meaningful properties are inherently guaranteed by an arbitrary pixel space ReLU network. We believe this approach can be a helpful addition in comprehensively evaluating the reliability of a network via quantifying its degree of compliance to an ideal specification.

The paper focuses on the problem of raw data fusion in neural networks based 3D object detection architectures. Here we consider the case of autonomous driving with data from camera and LiDAR sensors. Understanding the vehicle surroundings is a crucial task in autonomous driving since any subsequent action taken is strongly dependent on it. In this paper we present an alternative method of fusing camera image information with LiDAR poinclouds at a close to raw level of abstraction. Our results suggest that our approach improves the average precision of 3D bounding box detection of cyclists (and possibly other objects) in sparse point clouds compared to the baseline architecture without low-level fusion. The proposed approach has been evaluated on the KITTI dataset containing driving scenes with corresponding camera and LiDAR data. The long-term goal of our research is to develop a neural network architecture for environment perception that fuses multi-sensor data at the earliest stages possible, thus leveraging the full benefits of possible inter-sensor synergies.