skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: AVMaestro: A Centralized Policy Enforcement Framework for Safe Autonomous-driving Environments
Autonomous vehicles (AVs) are on the verge of changing the transportation industry. Despite the fast development of autonomous driving systems (ADSs), they still face safety and security challenges. Current defensive approaches usually focus on a narrow objective and are bound to specific platforms, making them difficult to generalize. To solve these limitations, we propose AVMaestro, an efficient and effective policy enforcement framework for full-stack ADSs. AVMaestro includes a code instrumentation module to systematically collect required information across the entire ADS, which will then be feed into a centralized data examination module, where users can utilize the global information to deploy defensive methods to protect AVs from various threats. AVMaestro is evaluated on top of Apollo-6.0 and experimental results confirm that it can be easily incorporated into the original ADS with almost negligible run-time delay. We further demonstrate that utilizing the global information can not only improve the accuracy of existing intrusion detection methods, but also potentially inspire new security applications.  more » « less
Award ID(s):
1929771 1932464 2145493
PAR ID:
10359470
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
IEEE Intelligent Vehicles Symposium (IV)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    null (Ed.)
    Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors. 
    more » « less
  2. ; ; ; (, Proceedings of the ACM on Software Engineering)
    Autonomous driving systems (ADS) require extensive testing and validation before deployment. However, it is tedious and time-consuming to construct traffic scenarios for ADS testing. In this paper, we propose TrafficComposer, a multi-modal traffic scenario construction approach for ADS testing. TrafficComposer takes as input a natural language (NL) description of a desired traffic scenario and a complementary traffic scene image. Then, it generates the corresponding traffic scenario in a simulator, such as CARLA and LGSVL. Specifically, TrafficComposer integrates high-level dynamic information about the traffic scenario from the NL description and intricate details about the surrounding vehicles, pedestrians, and the road network from the image. The information from the two modalities is complementary to each other and helps generate high-quality traffic scenarios for ADS testing. On a benchmark of 120 traffic scenarios, TrafficComposer achieves 97.0% accuracy, outperforming the best-performing baseline by 7.3%. Both direct testing and fuzz testing experiments on six ADSs prove the bug detection capabilities of the traffic scenarios generated by TrafficComposer. These scenarios can directly discover 37 bugs and help two fuzzing methods find 33%–124% more bugs serving as initial seeds. 
    more » « less
  3. ; (, Proceedings of the 8th IEEE World Forum on Internet of Things (WF-IOT))
    Autonomous vehicles (AVs) are envisioned to enhance safety and efficiency on the road, increase productivity, and positively impact the urban transportation system. Due to recent developments in autonomous driving (AD) technology, AVs have started moving on the road. However, this promising technology has many unique security challenges that have the potential to cause traffic accidents. Though some researchers have exploited and addressed specific security issues in AD, there is a lack of a systematic approach to designing security solutions using a comprehensive threat model. A threat model analyzes and identifies potential threats and vulnerabilities. It also identifies the attacker model and proposes mitigation strategies based on known security solutions. As an emerging cyber-physical system, the AD system requires a well-designed threat model to understand the security threats and design solutions. This paper explores security issues in the AD system and analyzes the threat model using the STRIDE threat modeling process. We posit that our threat model-based analysis will help improve AVs' security and guide researchers toward developing secure AVs. 
    more » « less
  4. ; (, Sensors)
    null (Ed.)
    Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; (, Proceedings of the 23rd Annual International Workshop on Mobile Computing Systems and Applications)
    As we add more autonomous and semi-autonomous vehicles (AVs) to our roads, their effects on passenger and pedestrian safety are becoming more important. Despite extensive testing before deployment, AV systems are not perfect at identifying hazards in the roadway. Although a particular AV’s sensors and software may not be 100% accurate at identifying hazards, there is an untapped pool of information held by other AVs in the vicinity that could be used to quickly and accurately identify roadway hazards before they present a safety threat. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email