A major challenging issue related to the emerging mixed traffic vehicular system, composed of connected and automated vehicles (CAVs) together with human-driven vehicles, is the lack of adequate modeling and control framework, especially at traffic bottlenecks such as highway merging areas. A hierarchical control framework for merging areas is first outlined, where we assume that the merging sequence is decided by a higher control level. The focus of this paper is the lower level of the control framework that establishes a set of control algorithms for cooperative CAV trajectory optimization, defined for different merging scenarios in the presence of mixed traffic. To exploit complete cooperation flexibility of the vehicles, we identify six scenarios, consisting of triplets of vehicles, defined based on the different combinations of CAVs and conventional vehicles. For each triplet, different consecutive movement phases along with corresponding desired distance and speed set-points are designed. Through the movement phases, the CAVs engaged in the triplet cooperate to determine their optimal trajectories aiming at facilitating an efficient merging maneuver, while complying with realistic constraints related to safety and comfort of vehicle occupants. Distinct models are considered for each triplet, and a Model Predictive Control scheme is employed to compute the cooperative optimal control inputs, in terms of acceleration of CAVs, accounting also for human-driven vehicles’ uncertainties, such as drivers’ reaction time and desired speed tracing error. Simulation investigations demonstrate that the proposed cooperative merging algorithms ensure efficient and smooth merging maneuvers while satisfying all the prescribed constraints.

Cooperative merging control via trajectory optimization in mixed vehicular traffic

Shared fleets of fully automated vehicles (SAVs) coupled with real-time ride-sharing to and from transit stations are of interest to cities and nations in delivering more sustainable transportation...

Use of Shared Automated Vehicles for First-Mile Last-Mile Service: Micro-Simulation of Rail-Transit Connections in Austin, Texas:

Investigation of acceptance of driverless buses in the city of Trikala and optimization of the service using Conjoint Analysis

This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose an Occupant's Preference Metric (OPM) which is defining a preferred lateral and longitudinal acceleration region with maximum allowable jerk for users. Moreover, we propose a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles. The proposed system not only provides the criteria for the occupant's driving preference, but also provides a personalized autonomous self-driving style like a human driver instead of a Robocar. The simulation and experimental results demonstrated that the proposed system can maneuver the self-driving vehicle like a human driver by tracking the specified criterion of admissible acceleration and jerk.

Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles.

Research on the use of autonomous vehicles as a mode of public transport in a city context is lacking. This paper focuses on the use of recently established autonomous buses (self-driving electric shuttle buses) running along a regular public transport line in a residential area of Oslo, Norway. We use a mixed-methods approach based on survey and interview data from two independent studies. The paper examines intentions to use autonomous buses before and after these were introduced in the case area as well as how passengers experience traveling by autonomous bus. Results show that the intention to use the autonomous buses was mostly positive both before and after using them. Most users felt safe while traveling by autonomous bus. Two suggestions for improvement made by the users were to: increase the speed and reduce the abrupt breaking of the autonomous buses. Overall, outcomes from this paper suggest that residents would be willing to use autonomous buses if these offer more frequent bus departures than the existing ones. However, as full automation has not been achieved yet and there is a host on board who can control the vehicle if necessary, passenger experiences and intentions to use should be reassessed with fully automated buses in future studies.

Autonomous buses: Intentions to use, passenger experiences, and suggestions for improvement

The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.

https://www.mdpi.com/2079-9292/8/9/943/pdf

Jeongseok Seo

Papers

Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus