Browse Topic: Advanced driver assistance systems (ADAS)
In the two months since Microvision bought Luminar and acquired key tech and talent, the sensor company has been busy. In that time, they've merged key lidar units from each company and created a perception software stack to run it in a convincing demo of its ADAS and autonomous capabilities. The company is also pushing innovative lidar tech into the defense drone and antidrone markets, already working with a German defense supplier that works with NATO member countries.
Bird accidental collision with overhead transmission lines poses a threat to the ecology of rare bird populations. This article analyzes the warning measures to prevent birds from accidental collisions at home and abroad. In response to the low efficiency of manual installation and the poor static warning effect in preventing birds from accidental collisions with overhead transmission lines, the visual characteristics of birds are analyzed. A drone-based automatic installation flash-type bird accidental collision warning device is proposed, which includes a fixture, a disc, and a luminous circuit. The fixture can be carried and installed on the overhead line by a drone and can be easily disassembled. The disc adopts eye-catching colors and has a hollow structure to reduce wind resistance load. The luminous circuit includes solar panels, charge and discharge control circuits, flicker control circuits, batteries, and luminous components. The drone suspension warning device test was conducted, and the results showed that the device can be easily suspended from the overhead line by the drone.
Microchip Technology and Hyundai Motor Group recently announced a collaboration to test 10BASE-T1S Single Pair Ethernet (SPE) technology for advanced in-vehicle networks to provide improved ADAS and connected-vehicle features. HMG told SAE Media it is working with multiple technology partners to review the overall applicability of 10BASE-T1S technology and hopes 10BASE-T1S can help optimize the deployment of gateways and switches. The technology's ethernet-based networking concepts might also contribute to simplifying network design and implementation for future zonal architectures. We also spoke with Matthias Kaestner, corporate vice president of Microchip Technology's data center, networking and automotive business units, about the partnership, via email.
Driving in San Francisco can be a challenge. When Mercedes driver Christoph von Hugo - who is on the Development Advanced Driver Assistance Systems team - turns on the company's new MB. Drive Assist Pro, I expect it to disengage within minutes. Instead, the 2027 electric CLA makes the same decisions most drivers would make. A pedestrian looks like they are about to walk into the street from the middle of the block. Immediately, the car reacts by moving over. It's subtle. But don't call it autonomous driving. Mercedes says it's more of a co-driver.
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test Drive (VTD). By developing an integrated solution for acquiring driver response, creation of scenarios and evaluation of control systems, this paper focuses on virtual validation of systems in a time saving and cost-effective manner.
Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis, including screen transitions, animations, and error messages. Among the methods used are optical character recognition (OCR) for on-screen text validation, convolutional neural networks (CNNs) for screen classification, and object detection for user interface verification. Automated anomaly detection and interface behavior evaluation are made easier with this method. Phase 2 suggests integrating a Hardware-in-the-Loop (HIL) simulator to transform the system into a closed-loop testing environment. The vision-based system will assess system responsiveness and end-to-end behavior, while the HIL setup will produce simulated user inputs and vehicle network data (such as CAN, Ethernet). This thorough framework tackles important issues like complex system integration, multimodal interaction testing, and managing cognitive load. In order to support the creation of safer, more user-friendly infotainment and digital cluster systems that are in line with Advanced Driver Assistance Systems (ADAS) standards, it seeks to decrease the amount of manual testing effort, increase test coverage, and improve consistency.
Traditionally, occupant safety research has centered on passive safety systems such as seatbelts, airbags, and energy-absorbing vehicle structures, all designed under the assumption of a nominal occupant posture at the moment of impact. However, with increasing deployment of active safety technologies such as Forward Collision Warning (FCW) and Autonomous Emergency Braking (AEB), vehicle occupants are exposed to pre-crash decelerations that alter their seated position before the crash. Although AEB mitigates the crash severity, the induced occupant movement leads to out-of-position behavior (OOP), compromising the available survival space phase and effectiveness of passive restraint systems during the crash. Despite these evolving real-world conditions, global regulatory bodies and NCAP programs continue to evaluate pre-crash and crash phases independently, with limited integration. Moreover, traditional Anthropomorphic Test Devices (ATDs) such as Hybrid III dummies, although highly repeatable, lack the bio-fidelity necessary to capture human-like kinematics during pre-crash braking events involving low g. ATDs do not simulate the spinal articulation, posture adjustments and active muscle contraction that occur during emergency maneuvers or pre-crash scenarios. To overcome these limitations, researchers have increasingly turned to Human Body Models (HBMs) such as Total Human Model for Safety (THUMS) and Global Human Body Model Consortium (GHBMC). These models enable high-fidelity finite element (FE) simulations with anatomical realism, allowing for the inclusion of active musculature and posture changes. This study aims to quantify the occupant forward excursion under pre-crash phase (due to AEB) and explore the possibility of an integrated simulation framework that evaluates occupant safety across both pre-crash and crash events. For this, the approach was to carry out full vehicle braking tests (1g braking pulse) with adult male (AM50) volunteers at different speeds to measure forward head excursion during pre-crash. These scenarios were replicated in LS-Dyna using THUMS HBM, showing strong agreement with experimental data. The resulting excursed postures were then used in crash simulations with ATDs to evaluate the effect on injury outcomes. Overall, the findings demonstrate effect of forward excursion on occupant injuries and the effectiveness of HBMs in capturing occupant kinematics, during pre-crash events.
The high-pressure steering hose in a hydraulic steering system carries pressurized hydraulic fluid from the power steering pump to the steering gear (or steering rack). Its main function is to transmit the force generated by the pump so that the hydraulic pressure assists the driver in turning the wheels more easily. The high-pressure hydraulic pipeline in the power steering system is a vital component for ensuring optimal performance. During warranty analysis, leakage incidents were observed at the customer end within the warranty period. The primary factors contributing to these failures include pipe material thickness, material composition, mechanical properties, and engine-induced vibrations. This study investigates fatigue-related failures through detailed material characterization and Computer-Aided Engineering (CAE) based on real world usage road load data collected. The objective is to identify the root causes by examining the influence of varying pipe thickness on fatigue life. The investigation discovered that crack initiation predominantly occurred on the concave side of bent pipe sections, specifically on the engine-side high-pressure steering line, which is connected to the power steering pump mounted on the engine. Fracture surfaces exhibited characteristics consistent with fatigue failure, with crack propagation primarily oriented longitudinally along the pipe. The highest tangential stresses were observed on the out word, resulting from the combined effects of internal hydraulic pressure and vibrational loads. Fatigue cracks originated from the inner surface and propagated outward under cyclic stresses induced by pressure fluctuations and engine vibrations during vehicle operation on the road. Computer-Aided Engineering (CAE) simulations indicated that the failure mechanism was primarily attributable to an incorrect material thickness selection during the development phase. Modifications to the pipe design, including increased material thickness, were implemented, leading to improved performance in subsequent testing. The high-pressure hydraulic pipeline exhibits decreased failure rates and improved reliability and durability following the implementation of the revised design.
In the Indian context, introduction of ADAS can play a positive role in improving road safety by assisting the driver and preventing unsafe driver behaviour. Technologies like Automated Emergency Braking (AEB), Lane Keep System, Adaptive Cruise Control, Driver Drowsiness Detection, Driver Alcohol detection etc., if deployed safely and used in a safe manner can help prevent many of the current road deaths in India. Safe deployment and safe use of such ADAS technologies require the systems to operate without failure within their operational design domains (ODD) and not surprise the drivers with sudden or unpredictable failures, to help develop their trust in the technology. As a result, identifying test scenarios remain a key step in the development of Advanced Driver Assistance Systems (ADAS). This remains a challenge due to the large test space especially for the Indian context due to the unpredictable traffic behaviour and occasional road infrastructure. In this paper, we introduce a novel open-access crowd-sourcing public platform, Safety Pool™ Studio, to enable crowdsourcing of traffic scenarios in the Indian context. Safety Pool™ Studio platform enables any member of the public or the road traffic ecosystem (e.g. traffic police, local authorities, academia etc.) to create a traffic scenario using a graphical interface, like a LEGO making exercise. This would enable the users to share their real-life experiences of traffic scenarios in a simple, accessible and inclusive manner and contribute to a global pool of traffic scenarios in the Indian context. Safety Pool™ Studio provides multi-language support for India’s regional languages like Hindi, Bengali, Tamil, Marathi, Punjabi, Kannada, Telugu, Gujrati among others. Safety Pool™ Studio has been developed in a way the graphical scenarios can automatically be converted into programmatic description of scenarios for traditional simulation-based testing of ADAS.
This study presents a structured evaluation framework for reasonably foreseeable misuse in automated driving systems (ADS), grounded in the ISO 21448 Safety of the Intended Functionality (SOTIF) lifecycle. Although SOTIF emphasizes risks that arise from system limitations and user behavior, the standard lacks concrete guidance for validating misuse scenarios in practice. To address this gap, we propose an end-to-end methodology that integrates four components: (1) hazard modeling via system–theoretic process analysis (STPA), (2) probabilistic risk quantification through numerical simulation, (3) verification using high-fidelity simulation, and (4) empirical validation via driver-in-the-loop system (DILS) experiments. Each component is aligned with specific SOTIF clauses to ensure lifecycle compliance. We apply this framework to a case of driver overreliance on automated emergency braking (AEB) at high speeds—a condition where system intervention is intentionally suppressed. Initial numerical analysis suggested that the scenario narrowly satisfies the acceptance criteria. Applying the proposed framework to this scenario reveals that significant safety risks can persist even when the system functions according to its design intent. Our findings demonstrate that foreseeable misuse can be formally modeled, simulated, and empirically validated within the SOTIF framework. The proposed approach enables system developers to quantify behavioral risk and assess human-centered edge cases with greater rigor. This work contributes to operationalizing SOTIF for behavioral safety assurance and lays the foundation for future research on risk mitigation through adaptive HMI and context-aware alerts.
Simulation has become mission-critical for ADAS development. Model-based systems engineering can integrate modeling and simulation from the start of the design process. Advanced Driver Assistance Systems (ADAS) are transforming vehicle safety, acting as the bridge between conventional driving and full autonomy. From adaptive cruise control to emergency braking and blind-spot detection, these technologies rely on a dense network of radar sensors, antennas, electronic control units and software. What unites them is the need for precise functionality under complex real-world situations. Achieving full reliability requires more than testing on the road; it demands a virtual approach grounded in simulation. Simulation has become mission-critical for ADAS development. As new vehicles integrate dozens of sensors into tightly constrained spaces, even subtle design decisions can affect system performance. Radar solutions, in particular, present unique challenges, especially as vehicle surfaces grow more complex and the number of onboard systems increases.
This article suggests a validation methodology for autonomous driving. The goal is to validate front camera sensors in advanced driver-assist systems (ADAS) based on virtually generated scenarios. The outcome is the CARLA-based hardware-in-the-loop (HIL) simulation environment (CHASE). It allows the rapid prototyping and validation of the ADAS software. We tested this general approach on a specific experimental application/setup for a vehicle front camera sensor. The setup results were then proven to be comparable to real-world sensor performance. The CARLA simulation environment was used in tandem with a vehicle CAN bus interface. This introduced a significantly improved realism to user-defined test scenarios and their results. The approach benefits from almost unlimited variability of traffic scenarios and the cost-efficient generation of massive testing data.
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