Search Results

The successful deployment of automated vehicles (AVs) has recently coincided with the use of off-board sensors for assessments of operational safety. Many intersections and roadways have monocular cameras used primarily for traffic monitoring; however, monocular cameras may not be sufficient to allow for useful AV operational safety assessments to be made in all operational design domains (ODDs) such as low ambient light and inclement weather conditions. Additional sensor modalities such as Light Detecting and Ranging (LiDAR) sensors allow for a wider range of scenarios to be accommodated and may also provide improved measurements of the Operational Safety Assessment (OSA) metrics previously introduced by the Institute of Automated Mobility (IAM).

As the deployment of automated vehicles (AVs) on public roadways expands, there is growing interest in establishing metrics that can be used to evaluate vehicle operational safety. The set of Operational Safety Assessment (OSA) metrics, that include several safety envelope-type metrics, previously proposed by the Institute of Automated Mobility (IAM) are a step towards this goal. The safety envelope OSA metrics can be computed using kinematics derived from video data captured by infrastructure-based cameras and thus do not require on-board sensor data or vehicle-to-infrastructure (V2I) connectivity, though either of the latter data sources could enhance kinematic data accuracy. However, the calculation of some metrics includes certain vehicle-specific parameters that must be assumed or estimated if they are not known a priori or communicated directly by the vehicle.

The operational safety of Automated Driving System (ADS)-Operated Vehicles (AVs) are a rising concern with the deployment of AVs as prototypes being tested and also in commercial deployment. The robustness of safety evaluation systems is essential in determining the operational safety of AVs as they interact with human-driven vehicles. Extending upon earlier works of the Institute of Automated Mobility (IAM) that have explored the Operational Safety Assessment (OSA) metrics and infrastructure-based safety monitoring systems, in this work, we compare the performance of an infrastructure-based Light Detection And Ranging (LIDAR) system to an onboard vehicle-based LIDAR system in testing at the Maricopa County Department of Transportation SMARTDrive testbed in Anthem, Arizona. The sensor modalities are located in infrastructure and onboard the test vehicles, including LIDAR, cameras, a real-time differential GPS, and a drone with a camera.

The operational safety of automated driving system (ADS)-equipped vehicles (AVs) needs to be quantified for an understanding of risk, requiring the measurement of parameters as they relate to AVs and human driven vehicles alike. In prior work by the Institute of Automated Mobility (IAM), operational safety metrics were introduced as part of an operational safety assessment (OSA) methodology that provide quantification of behavioral safety of AVs and human-driven vehicles as they interact with each other and other road users. To calculate OSA metrics, the data capture system must accurately and precisely determine position, velocity, acceleration, and geometrical relationships between various safety-critical traffic participants. The design of an infrastructure-based system that is intended to capture the data required for calculation of OSA metrics is addressed in this paper.

The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives.