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Cooperative Adaptive Cruise Control – Part 1: Overview and Attacks


In this demonstrator, we investigate how control systems in automated vehicles can be attacked. We explore this with a relatively simple automated driving function, namely Cooperative Adaptive Cruise Control (CACC). In CACC, vehicles periodically communicate their current driving state (like position, speed, heading, acceleration, or deceleration) to other vehicles with which they form a platoon of vehicles. A CACC control algorithm running in each vehicle uses this information to determine the necessary longitudinal acceleration or deceleration that is required to maintain a stable platoon. Compared to the better-known Adaptive Cruise Control (ACC) that typically relies on RADAR-based distance measurements to the vehicle in front, CACC has the advantages that a better string-stability and shorter driving distances can be achieved.

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Automotive Pentesting Guide


The task of the Automotive Pentesting Guide is to provide tool-based support for carrying out a security test in the automotive sector. The goal is to clearly define the scope to test for all parties involved, to run the test in a uniformly structured and repeatable manner, and to record the results in a clean and comparable manner.

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Self-Assessment in Tracking Algorithms


Self-assessment is a key to safety and security in automated driving. In order to design safer as well as more robust and secure automated driving functions, the goal is to self-assess the performance of each module in a whole automated driving system. One crucial component in automated driving systems is the tracking of surrounding objects, where the Kalman filter is the most fundamental tracking algorithm.

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Anomaly Detection in Radar Data


For autonomous driving, radar is an important sensor type. On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment. On the other hand, in literature, radar sensors are known for their robustness against several kinds of adverse weather conditions. However, on the downside, radar is susceptible to ghost targets or clutter which can be caused by several different causes, e.g., reflective surfaces in the environment but also by intelligent and modern attacks. Ghost targets, for instance, can result in erroneous object detections. To this end, it is desirable to identify anomalous targets as early as possible in radar data to ensure safety and security of the sensor data.

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Generation of Artificial Radar Targets


In order to guarantee the safety and security of autonomous vehicles, rigorous tests and verifications are required. Analogously to a crash test center, to ensure the integrity specifically of the sensors employed in autonomous cars, an environment suited for intense testing of the sensors employed in autonomous cars can be envisioned. In such a test center the effectiveness of the security measures and safety-relevant features is investigated.

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AP1 Demonstrator


This video shows you some of the results of the SecForCARs work package 1 (AP1).

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SecForCARs Project Overview


Since 2018, the German SecForCARs project investigates the security of connected and automated vehicles. With its 14 partners from industry and academia, it addresses a broad range of challenges, ranging from analysis of the extended attack surface of connected, automated vehicles that includes attacks on vehicle systems, sensor attacks, for example on its RADAR sensors, or attacks that try to manipulate its data fusion and control algorithms.

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