As autonomous cars are rolled out into new environments, their ability to solve the simultaneous localization and mapping (SLAM) problem becomes critical. In order to tackle this problem, autonomous vehicles rely on sensor suites that provide them with information about their operating environment. When large scale production is taken into consideration, a trade-off between an acceptable sensor suite cost and its resulting performance characteristics arises. Furthermore, guaranteeing the system's performance requires a resilient sensor network design. This work seeks to address such trade-offs by introducing a method that takes into account the performance, cost, and resiliency of distinct sensor selections. As a result, this method is able to offer sensor combination recommendations based on the vehicle's operating environment. It is found that the structure of the environment influences sensor placement, and that the design of a resilient sensor network involves careful consideration of both environmental attributes such as landmark density and location, as well as the available types of complimentary sensors. Demonstration of the proposed approach is shown by evaluating it using sequences from the KITTI Benchmark Suite.
@cite_5: Abstract This paper presents a comprehensive review on sensor modalities currently in used for solving the Simultaneous Localization and Mapping (SLAM) problem. The review focuses on SLAM for mobile robots in a variety of environments. The strengths and weaknesses of acoustic modality sensors such as ultrasonic and sonar sensors, laser range finders, visual sensors such as stereo vision sensors, and RGB-D sensors like the Microsoft Kinect and the Asus Xtion Pro Live are compared based on current usage in published research papers. Based on this review, we propose that RGB-D sensors have unique advantages which make them particularly suitable for SLAM problems.
@cite_14: This paper presents the vision of the Artificial Vision and Intelligent Systems Laboratory (VisLab) on future automated vehicles, ranging from sensor selection up to their extensive testing. VisLab's design choices are explained using the BRAiVE autonomous vehicle prototype as an example. BRAiVE, which is specifically designed to develop, test, and demonstrate advanced safety applications with different automation levels, features a high integration level and a low-cost sensor suite, which are mainly based on vision, as opposed to many other autonomous vehicle implementations based on expensive and invasive sensors. The importance of performing extensive tests to validate the design choices is considered to be a hard requirement, and different tests have been organized, including an intercontinental trip from Italy to China. This paper also presents the test, the main challenges, and the vehicles that have been specifically developed for this test, which was performed by four autonomous vehicles based on BRAiVE's architecture. This paper also includes final remarks on VisLab's perspective on future vehicles' sensor suite.
@cite_26: The safety validation of automated driving of SAE level 3 and higher (AD) is still an unsolved issue. In the validation process, criticality metrics can be used for two different purposes. First, for the identification of test scenarios from recorded data that are later tested in simulation. Secondly, for an estimation of the safety of a specific AD system based on the likelihood of critical situations in test drives or in other words as a safety surrogate. In the past, different metrics for those purposes have been defined that work well in specific scenarios such as longitudinal traffic. However, a metric that describes criticality in all situations and is applicable to human and AD traffic is currently not available. In this paper, an approach to define a criticality metric is introduced. The metric is based on the definition of criticality as the level of driving requirements in the specific situation. The computation of the proposed metric uses elements of model predictive control using an objective function that contains four elements that describe the difficulty of the driving task. Based on those demands and a simplified driving dynamics model, the solution with the minimal criticality is computed. Finally, the metric is tested in four test scenarios that are typical for highway traffic. A short parameter variation study is conducted in order to study certain effects of the algorithm and to identify room for improvement.
In recent years, research and development efforts around autonomous cars have shifted towards producing certifiable systems capable of being deployed into multiple environments @cite_26 . Naturally, sensors play a primordial role within this system by enabling the vehicle to interact with and to account for the ever-changing operational scenarios. Unfortunately, the plethora of available sensor types @cite_5 , in addition to their possible multiplicity and physical location within the vehicle's system, lead to a combinatorial number of possible sensor suite designs from which to choose. This poses both a design and a testing problem, since it becomes monetarily infeasible to iterate through all possible physical prototypes and computationally intractable to account for every combination in driving simulations @cite_14 .