Browse Topic: Level 3 (Conditional driving automation)
ABSTRACT The automotive and defense industries are going through a period of disruption with the advent of Connected and Automated Vehicles (CAV) driven primarily by innovations in affordable sensor technologies, drive-by-wire systems, and Artificial Intelligence-based decision support systems. One of the primary tools in the testing and validation of these systems is a comparison between virtual and physical-based simulations, which provides a low-cost, systems-approach testing of frequently occurring driving scenarios such as vehicle platooning and edge cases and sensor-spoofing in congested areas. Consequently, the project team developed a robotic vehicle platform—Scaled Testbed for Automated and Robotic Systems (STARS)—to be used for accelerated testing elements of Automated Driving Systems (ADS) including data acquisition through sensor-fusion practices typically observed in the field of robotics. This paper will highlight the implementation of STARS as a scaled testbed for rapid
A look at who's doing what when it comes to sensors for an L3 world. SAE Level 3 automated driving marks a clear break from the lower levels of driving assistance since that is the dividing line where the driver can be freed to focus on other things. While the driver may sometimes be required to take control again, responsibility in an accident can be shifted from the driver to the automaker and suppliers. Only a few cars have met regulatory approval for Level 3 operation. Thus far, only Honda (in Japan), the Mercedes-Benz S-Class and EQS sedans with Drive Pilot and BMW's recently introduced 7 Series offer Level 3 autonomy. With more vehicles getting L3 technology and further automated driving skills being developed, we wanted to check in with some of the key players in this tech space and hear the latest industry thinking about best practices for ADAS and AV Sensors
In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm, which is further enhanced through the integration of wheel encoder and altitude data. Based on the generated map a LiDAR localization algorithm is used to determine the pose of the vehicle. The FAST-LIO based localization algorithm is enhanced by integrating relative LiDAR Odometry estimates and by using a simple yet effective delay compensation method to
On-road vehicles equipped with driving automation features are entering the mainstream public space. This category of vehicles is now extending to include those where a human might not be needed for operation on board. Several pilot programs are underway, and the first permits for commercial usage of vehicles without an onboard operator are being issued. However, questions like “How safe is safe enough?” and “What to do if the system fails?” persist. This is where remote operation comes in, which is an additional layer to the automated driving system where a human assists the so-called “driverless” vehicle in certain situations. Such remote-operation solutions introduce additional challenges and potential risks as the entire chain of “automated vehicle, communication network, and human operator” now needs to work together safely, effectively, and practically. And as much as there are technical questions regarding network latency, bandwidth, cybersecurity, etc., aspects like human
The impending deployment of automated vehicles (AVs) represents a major shift in the traditional approach to ground transportation; its effects will inevitably be felt by parties directly involved with vehicle manufacturing and use (e.g., automotive original equipment manufacturers (OEMs), public transportation systems, heavy goods transportation providers) and those that play roles in the mobility ecosystem (e.g., aftermarket and maintenance industries, infrastructure and planning organizations, automotive insurance providers, marketers, telecommunication companies). The focus of this chapter is to address a topic overlooked by many who choose to view automated driving systems and AVs from a “10,000-foot perspective:” the topic of how AVs will communicate with other road users such as conventional (human-driven) vehicles, bicyclists, and pedestrians while in operation. This unsettled issue requires assessing the spectrum of existing modes of communication—both implicit and explicit
On-road vehicles equipped with driving automation features are entering the mainstream public space. This category of vehicles is now extending to include those where a human might not be needed for operation on board. Several pilot programs are underway, and the first permits for commercial usage of vehicles without an onboard operator are being issued. However, questions like “How safe is safe enough?” and “What to do if the system fails?” persist. This is where remote operation comes in, which is an additional layer to the automated driving system where a human assists the so-called “driverless” vehicle in certain situations. Such remote-operation solutions introduce additional challenges and potential risks as the entire chain of “automated vehicle, communication network, and human operator” now needs to work together safely, effectively, and practically. And as much as there are technical questions regarding network latency, bandwidth, cybersecurity, etc., aspects like human
This study assessed a driver’s ability to safely manage Super Cruise lane changes, both driver commanded (Lane Change on Demand, LCoD) and system triggered Automatic Lane Changes (ALC). Data was gathered under naturalistic conditions on public roads in the Washington, D.C. area with 12 drivers each of whom were provided with a Super Cruise equipped study vehicle over a 10-day exposure period. Drivers were shown how to operate Super Cruise (e.g., system displays, how to activate and disengage, etc.) and provided opportunities to initiate and experience commanded lane changes (LCoD), including how to override the system. Overall, drivers experienced 698 attempted Super Cruise lane changes, 510 Automatic and 188 commanded LCoD lane changes with drivers experiencing an average of 43 Automatic lane changes and 16 LCoD lane changes. Analyses characterized driver interactions during LCoD and ALC maneuvers exploring the extent to which drivers actively monitor the process and remain engaged
Advanced Autonomous Vehicles (AV) for SAE Level 3 and Level 4 functions will lead to a new understanding of the operation phase in the overall product lifecycle. Regulations such as the EU Implementing Act and the German L4 Act (AFGBV) request a continuous field surveillance, the handling of critical E/E faults and software updates during operation. This is required to enhance the Operational Design Domain (ODD) during operation, offering Functions on Demand (FoD), by increasing software features within these autonomous vehicle systems over the entire digital product lifecycle, and to avoid and reduce downtime by a malfunction of the Autonomous Driving (AD) software stack. Supported by implemented effective management systems for Cyber Security (R155), Software Update Management System (R156) and a Safety Management System (SMS) (in compliance to Automated Lane Keeping System (ALKS) (R157)), the organizations have to ensure safe and secure development, deployment and operation to
Letter from the Special Issue Editor
Recent rapid advancement in machine learning (ML) technologies have unlocked the potential for realizing advanced vehicle functions that were previously not feasible using traditional approaches to software development. One prominent example is the area of automated driving. However, there is much discussion regarding whether ML-based vehicle functions can be engineered to be acceptably safe, with concerns related to the inherent difficulty and ambiguity of the tasks to which the technology is applied. This leads to challenges in defining adequately safe responses for all possible situations and an acceptable level of residual risk, which is then compounded by the reliance on training data. The Path to Safe Machine Learning for Automotive Applications discusses the challenges involved in the application of ML to safety-critical vehicle functions and provides a set of recommendations within the context of current and upcoming safety standards. In summary, the potential of ML will only
Future vehicle systems will feature a reduced sensor array, but still will need a technology combination for safe performance. Despite the industrywide realization that SAE driving automation Levels 4 and 5 are not imminent and instead long-term goals, development continues on the sensors that power current and future ADAS systems and up to Level 3. Nothing made it more clear that lidar was the industry favorite than the 30-plus companies showing versions of the tech at the 2023 Consumer Electronics Show. That's an unstainable number, say industry experts. They see the next few years consisting of consolidation and many companies leaving the market
“The future happened yesterday” is an appropriate description of the rapid pace of development in automated-driving technology. The expression may be most accurate in sensor tech where, for most OEMs (except Tesla thus far), radar and lidar increasingly are considered an essential duo for enhanced automated driving beyond SAE Level 2, and of course for full Level 4-5 automation. Current lidar is transitioning from electro-mechanical systems to solid-state devices. Considered by industry experts to be the technology's inevitable future, Lidar 2.0 is next-generation 3D sensing that is software-defined, solid-state and scalable. Engineers believe those capabilities will make lidar ubiquitous by reducing costs, speeding innovation and improving user experience
ASI's Swarming technology allows collision-avoidance and other tests at high speeds on vehicles that human drivers find hard to match. A Utah company has developed a system to allow fully robotic testing of ADAS on production vehicles as one solution to the dangers of testing such systems with human drivers at high speeds and in real traffic. At the 2022 Automotive Testing Expo in Novi, Mich., ASI Automotive Product Manager Jed Judd talked about the system, called Swarming, and its control software Mobius. He said the company's development is a response to OEMs finding that simulation testing alone isn't enough for advanced ADAS. He also said that even professional human drivers have difficulty executing different test scenarios accurately due to what he called “a significant pucker factor” at high speeds
Every new industry sector goes through a consolidation process where the strongest survive, and so it is with automated and autonomous driving technologies. The recent shuttering of Argo AI, one of the autonomous-vehicle industry's leading tech companies, by Ford and Volkswagen might come as a surprise to commuters in San Francisco and in Phoenix, Arizona. Those who regularly use the robotaxi services of GM-backed Cruise Automation and Alphabet's Waymo see these and other AVs under development during their daily travels. On public roads. Every day. Indeed, Argo AI's demise (which insiders said was mainly due to friction among Ford and VW) and difficulties at other startups including AV pioneer Aurora, have highlighted the engineering challenges of safely achieving SAE Level 4 driving automation, while reinforcing AV critics. But as Guidehouse Insights' leading e-Mobility analyst Sam Abuelsamid notes in his Navigator column on page 3, the AV sector's leaders appear to be moving out
This document provides safety-relevant guidance for on-road testing of vehicles being operated by prototype conditional, high, and full (Levels 3 to 5) ADS, as defined by SAE J3016. It does not include guidance for evaluating the performance of post-production ADS-equipped vehicles. Moreover, this guidance only addresses testing of ADS-operated vehicles as overseen by in-vehicle fallback test drivers (IFTD). These guidelines do not address: Remote driving, including remote fallback test driving of prototype ADS-operated test vehicles in driverless operation. (Note: The term “remote fallback test driver” is included as a defined term herein and is intended to be addressed in a future iteration of this document. However, at this time, too little is published or known about this type of testing to provide even preliminary guidance.) Testing of driver support features (i.e., Levels 1 and 2), which rely on a human driver to perform part of the dynamic driving task (DDT) and to supervise the
Two experiments were carried out to clarify the characteristics of manual driving when the task of vehicle control is transferred from an autonomous driving system at SAE levels 3 and 5 to manual driving. The first experiment involved another vehicle merging into the lane of the host vehicle from the left side of a highway. This experiment simulated the functional limit of a level 3 system with the driver in a situation of low alertness. When the other vehicle changed lane in front of the host vehicle, the driving task was transferred from the system to the driver. The second experiment simulated a driver travelling along a city road with manual driving after the driver used the system in a situation of sleeping on a highway. In this experiment, a pedestrian emerges from a blind spot along a city road, and the driver needs to brake having recently awaken. In the first experiment, the driver with low alertness could not control the vehicle when manually driving. In the second experiment
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