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Autonomous vehicles and advanced driver assistance systems have functionality realized across numerous distributed systems that interact with a dynamic cyber-physical environment. This complexity raises the potential for emergent behaviours which are not intended for the system’s operational use. The need to analyze the intended functionality of these emergent behaviours for potential hazards, which may occur in absence of faults, are aspects of the ISO PAS 21448, Safety of the Intended Functionality (SOTIF) [1]. The Safety of the Intended Functionality or SOTIF is a framework for developing systems which are free from unreasonable risk due to the intended functionality or performance limitations of a system which is free from faults. This is meant to complement Functional Safety which is covered in ISO 26262 [2]. The major focus of SOTIF is to aid in the functional development of a system.

As intelligent automated vehicle technologies evolve, there is a greater need to understand and define the role of the human user, whether completely hands-off (L5) or partly hands-on. At all levels of automation, the human occupant may feel anxious or ill-at-ease. This may reflect as higher stress/workload. The study in this paper further refines how perceived workload may be determined based on occupant physiological measures. Because of great variation in individual personalities, age, driving experiences, gender, etc., a generic model applicable to all could not be developed. Rather, individual workload models that used physiological and vehicle measures were developed.

As automakers begin to design the autonomous vehicle (AV) for the first time, they must reconsider customer interaction with the Automatic Speech Recognition (ASR) system carried over from the traditional vehicle. Within an AV, the voice-to-ASR system needs to be capable of serving a customer located in any seat of the car. These shifts in focus require changes to the microphone selection and placement to serve the entire vehicle. Further complicating the scenario are new sources of noise that are specific to the AV that enable autonomous operation. Hardware mounted on the roof that are used to support cameras and LIDAR sensors, and mechanisms meant to keep that hardware clean and functioning, add even further noise contamination that can pollute the voice interaction. In this paper, we discuss the ramifications of picking up the intended customer’s voice when they are no longer bound to the traditional front left “driver’s” seat.

There has been a growing concern about increasing vehicle-mile traveled (VMT) associated with deadhead trips for dynamic rideshare services, particularly with the emergence of Shared Autonomous Vehicle (SAV) services. Studies in the literature on repositioning strategies have been limited to synthetic or small-scale study areas. This study considers a large-scale computational experiment involving a New York City study area with a network of 16,782 nodes and 23,337 links with 662,455 potential travelers from the 2016 Yellow Taxi data. We investigate the potential to reduce VMT and deadhead miles for dynamic rideshare operations combined with vehicle repositioning strategies. Three repositioning strategies are evaluated: (1) Roaming around areas with higher pickup probabilities to maximize the chance of picking up passengers, (2) Staying at curb side after completing trips, and (3) Repositioning to depots to minimize deadhead trips.

The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.

The advancement towards development of autonomy follows either the bottom-up approach of gradually improving and expanding existing Advanced Driver Assist Systems (ADAS) technology where the driver is present in the control loop or the top-down approach of directly developing Autonomous Vehicles (AV) hardware and software using alternative approaches without the driver present in the control loop. Most ADAS systems today fall under the classification of SAE Level 1 which is also referred to as the driver assistance level. The progression from SAE Level 1 to SAE Level 2 or partial automation involves the critical task of merging autonomous lateral control and autonomous longitudinal control such that the tasks of steering and acceleration/deceleration are not required to be handled by the driver under certain conditions [1].

The automotive industry is heading towards the path of autonomy with the development of autonomous vehicles. An autonomous vehicle consists of two main components. The first is the software which is responsible for the decision-making capabilities of the system. The second is the hardware which encompasses all aspects of the physical vehicle which are responsible for vehicle motion such as the engine, brakes and steering subsystems along with their corresponding controls. This component forms the basis of the autonomous vehicle platform. For SAE Level 4 autonomous vehicles, where an automated driving system is responsible for all the dynamics driving tasks including the fallback driving performance in case of system faults, redundant mechanical systems and controls are required as part of the autonomous vehicle platform since the driver is completely out of the loop with respect to driving.