Localization in high-level Autonomous Driving (AD) systems is highly security critical. Recently, researchers found that state-of-the-art Multi-Sensor Fusion (MSF) based localization is vulnerable to GPS spoofing, which can cause road hazards such as driving off road or onto the wrong way. In this work, we perform the first exploration of using Lane Detection (LD) to detect and correct deviations caused by such attacks and design a novel LD-based system-level defense, LD3. We evaluate LD3 on real-world sensor traces and find that it can achieve effective and timely detection against the state-of-the-art attack with 100% true positive rates and 0% false positive rates. Results show that LD3 can be highly effective at steering the AD vehicle to safely stop within the current traffic lane. We implement LD3 on 2 open-source AD systems and validate its end-to-end defense capability using an industry-grade AD simulator and also in the physical world with a real vehicle-sized AD R&D vehicle.

Connected Vehicle (CV) technologies are under rapid deployment across the globe and will soon reshape our transportation systems, bringing benefits to mobility, safety, environment, etc. Meanwhile, such technologies also attract attention from cyberattacks. Recent work shows that CV-based Intelligent Traffic Signal Control Systems are vulnerable to data spoofing attacks, which can cause severe congestion effects in intersections. In this work, we explore a general detection strategy for infrastructure-side CV applications by estimating the trustworthiness of CVs based on readily-available infrastructure-side sensors. We implement our detector for the CV-based traffic signal control and evaluate it against two representative congestion attacks. Our evaluation in the industrial-grade traffic simulator shows that the detector can detect attacks with at least 95% true positive rates while keeping false positive rate below 7% and is robust to sensor noises.

GPS spoofing attacks pose great challenges to connected vehicle (CVs) safety applications and localization of autonomous vehicles (AVs). In this paper, we propose to utilize transportation and vehicle engineering domain knowledge to detect GPS spoofing attacks towards CVs and AVs. A novel detection method using learning from demonstration is developed, which can be implemented in both vehicles and at the transportation infrastructure. A computational-efficient driving model, which can be learned from historical trajectories of the vehicles, is constructed to predict normal driving behaviors. Then a statistical method is developed to measure the dissimilarities between the observed trajectory and the predicted normal trajectory for anomaly detection. We validate the proposed method using two threat models (i.e., attacks targeting the multi-sensor fusion system of AVs and attacks targeting the intersection movement assist application of CVs) on two real-world datasets (i.e., KAIST and Michigan roundabout dataset). Results show that the proposed model is able to detect almost all of the attacks in time with low false positive and false negative rates.

The security of the Autonomous Driving (AD) system has been gaining researchers’ and public’s attention recently. Given that AD companies have invested a huge amount of resources in developing their AD models, e.g., localization models, these models, especially their parameters, are important intellectual property and deserve strong protection.

In the development of advanced driver-assistance systems (ADAS) and autonomous vehicles, machine learning techniques that are based on deep neural networks (DNNs) have been widely used for vehicle perception. These techniques offer significant improvement on average perception accuracy over traditional methods, however have been shown to be susceptible to adversarial attacks, where small perturbations in the input may cause significant errors in the perception results and lead to system failure. Most prior works addressing such adversarial attacks focus only on the sensing and perception modules. In this work, we propose an end-to-end approach that addresses the impact of adversarial attacks throughout perception, planning, and control modules. In particular, we choose a target ADAS application, the automated lane centering system in OpenPilot, quantify the perception uncertainty under adversarial attacks, and design a robust planning and control module accordingly based on the uncertainty analysis. We evaluate our proposed approach using both public dataset and production-grade autonomous driving simulator. The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attack and can achieve 55% ~ 90% improvement over the original OpenPilot.

Self-driving cars, or Autonomous Vehicles (AVs), are increasingly becoming an integral part of our daily life. About 50 corporations are actively working on AVs, including large companies such as Google, Ford, and Intel. Some AVs are already operating on public roads, with at least one unfortunate fatality recently on record. As a result, understanding bugs in AVs is critical for ensuring their security, safety, robustness, and correctness. While previous studies have focused on a variety of domains (e.g., numerical software; machine learning; and error-handling, concurrency, and performance bugs) to investigate bug characteristics, AVs have not been studied in a similar manner. Recently, two software systems for AVs, Baidu Apollo and Autoware, have emerged as frontrunners in the open-source community and have been used by large companies and governments (e.g., Lincoln, Volvo, Ford, Intel, Hitachi, LG, and the US Department of Transportation). From these two leading AV software systems, this paper describes our investigation of 16,851 commits and 499 AV bugs and introduces our classification of those bugs into 13 root causes, 20 bug symptoms, and 18 categories of software components those bugs often affect. We identify 16 major findings from our study and draw broader lessons from them to guide the research community towards future directions in software bug detection, localization, and repair.

Modern commercial multi-core processors are equipped with multiple hardware prefetchers on each core. The prefetchers can significantly improve application performance. However, shared resources, such as last-level cache (LLC) and off-chip memory bandwidth and controller, can lead to prefetch interference. Multiple techniques have been proposed to reduce such interference and improve the performance isolation across cores, such as coordinated control among prefetchers and cache partitioning (CP). Each of them has its advantages and disadvantages. This paper proposes combining these two techniques in a coordinated way. Prefetchers and LLC are treated as separate resources and a multi-resource management mechanism is proposed to control prefetching and cache partitioning. This control mechanism is implemented as a Linux kernel module and can be applied to a wide variety of prefetch architectures. An implementation on Intel Xeon E5 v4 processor shows that combining LLC partitioning and prefetch throttling provides a significant improvement in performance and fairness.

The trend of supporting wide vector units in general purpose microprocessors suggests opportunities for developing a new and elegant compilation approach to mitigate the impact of faults to cryptographic implementations, which we present in this work. We propose a compilation flow, CAMFAS, to automatically and selectively introduce vectorization in a cryptographic library - to translate a vanilla library into a library with vectorized code that is resistant to glitches. Unlike in traditional vectorization, the proposed compilation flow uses the extent of the vectors to introduce spatial redundancy in the intermediate computations. By doing so, without significantly increasing code size and execution time, the compilation flow provides sufficient redundancy in the data to detect errors in the intermediate values of the computation. Experimental results show that the proposed approach only generates an average of 26% more dynamic instructions over a series of asymmetric cryptographic algorithms in the Libgcrypt library.