S3-4.1 - Connected and automated vehicles

Is a roundabout still a good solution in the era where intelligent intersection control using vehicle connection (i-TLC), is in upswing? To answer this question, the capacity of i-roundabouts, where the infrastructure communicates with the vehicles (I2V), is determined analytically. The roundabouts considered are single-lane roundabouts and turbo roundabouts (a spiral multi-lane roundabout with reduced number of conflicts). A macroscopic approach explores the capacity gain that can be achieved by taking into account the necessary safety margins with regard to headways and gaps. Furthermore, it is assumed that by using I2V the speed, headway, and also the driving curve of the vehicles can be controlled. For roundabouts with speeds lower than 36 km/h, the conclusions are that: On a single lane roundabout, roughly a doubling of the capacity can be achieved. On a turbo roundabout the capacity gain can be surprisingly much higher (about a factor 2.5). This is due to the possibility of gap synchronization on the double-lane segments.

As automated vehicles become increasingly common on the road, the call for an appropriate preparation for its drivers is becoming more urgent. Expert opinions and insights have been acquired via a focus group discussion with eleven Dutch driving examiners to assist in inventorying what types of preparations are needed. The concept of meaningful human control (MHC) as an integral part of the discussion lead to consensual findings regarding ADAS functionality and the drivers tasks, as well as discussion topics on driver training and levels of automation. It was concluded to have more research into human factors to safeguard proper control over automated vehicles.

Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic car-following models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data. The results show that the optimal velocity-follow the leader (OV-FTL) model and the optimal velocity relative velocity model (OVRV) model are both able to reproduce the traffic flow oscillations, while the intelligent driver model (IDM) model requires substantially more noise in each driver's speed profile to exhibit the same waves.

In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the subject vehicle to detect sensor anomalies. We use the classic $chi^2$ fault detector in conjunction with the proposed AEKF for anomaly detection. To make the proposed model more suitable for real-world applications, we consider a stochastic communication time delay in the car-following model. Our experiments conducted on real-world connected vehicle data indicate that the AEKF with $chi^2$-detector can achieve a high anomaly detection performance.