In this paper, we investigated the performance of cooperative adaptive cruise control (CACC) algorithms in mixed traffic environments featuring connected automated vehicles (CAVs) and unconnected vehicles. For CAVs, we tested the recently proposed linear feedback control approach (Linear- CACCu) and adaptive model predictive control approach (A- MPC-CACCu) which have been tailored to extend CACC to mixed traffic environments. In contrast to most literature where CACC design and evaluation are performed on freeways, we focused on urban arterial roads using the CACC Field Operation Test Dataset from the Netherlands. We compared the performances of Linear-CACCu and A-MPC-CACCu to regular adaptive cruise control (ACC), where automated vehicles do not rely on connectivity, as well as human drivers. Performance comparison was done in terms of ego vehicle’s spacing error, acceleration, and energy consumption which relate to safety, driving comfort, and energy efficiency, respectively. Simulation results showed that CACCu algorithms significantly outper- formed the ACC and human drivers in these metrics. Moreover, we found that the fluctuations of the lead vehicle’s behavior due to changes in traffic signal phase have a significant impact on which CACCu is optimal (i.e., A-MPC-CACCu or Linear- CACCu). Thus, the CACC mode could be switched based on the expectation of traffic signal phase changes to assure better performance.
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Deep-Learning-Based Intrusion Detection for Autonomous Vehicle-Following Systems
Autonomous vehicle-following systems, including
Adaptive Cruise Control (ACC) and Cooperative Adaptive
Cruise Control (CACC), improve safety, efficiency, and string
stability for a vehicle (the ego vehicle) following its leading
vehicle. The ego vehicle senses or receives information, such
as the position, velocity, acceleration, or even intention, of
the leading vehicle and controls its own behavior. However,
it has been shown that sensors and wireless channels are
vulnerable to security attacks, and attackers can modify data
sensed from sensors or received from other vehicles. To address
this problem, in this paper, we design three types of stealthy
attacks on ACC or CACC inputs, where the stealthy attacks
can deceive a rule-based detection approach and impede system
properties (collision-freeness and vehicle-following distance).
We then develop two deep-learning models, a predictor-based
model and an encoder-decoder-based model to detect the
attacks, where the two models do not need attacker models for
training. The experimental results demonstrate the respective
strengths of different models and lead to a methodology for the
design of learning-based intrusion detection approaches.
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- Award ID(s):
- 1908549
- NSF-PAR ID:
- 10296350
- Date Published:
- Journal Name:
- 4th IEEE International Conference on Intelligent Transportation Systems - ITSC2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
