Browse Topic: Active safety systems
Sonatus and Omdia surveyed over 500 auto executives for their thoughts on SDVs, and the responses revealed that - once again - the future is unwritten. Data monetization as a top priority dropped from 51% to 44% (YoY) as automakers focus more on using data internally, with ADAS and product improvements rising six points to 41%. We spoke with Sonatus CMO John Heinlein about the survey for a recent episode of the SAE Automotive Engineering Podcast. You can listen via the links at the bottom of this Q&A, which is an edited version of our full discussion.
This paper proposes a nonlinear and robust State-Dependent Riccati Equation (SDRE) combined with H∞ control architecture for brake- by-wire systems, specifically designed to handle severe tire-road friction variations and μ-split scenarios. The primary objective is to maximize deceleration capabilities while rigorously maintaining yaw stability, trajectory tracking, and passenger comfort through jerk limitation. Situated within the domain of active safety, this research addresses robustness against real-world uncertainties by utilizing a high-fidelity 14-degree-of-freedom vehicle model that accounts for longitudinal, lateral, and yaw dynamics, suspension-induced pitch and roll effects, and nonlinear tire behavior with explicit load transfer. To ensure near-optimal slip tracking under variable surface conditions, the system employs online friction estimation via Extended and Unscented Kalman Filters (EKF/UKF) fusing wheel and IMU data to adaptively adjust slip targets. The control strategy is bifurcated: the SDRE component manages dominant nonlinearities through state-dependent gains to prevent wheel lock-up, while the H∞ component provides robust disturbance rejection against parametric uncertainties such as mass variations and sensor noise. Control efforts are distributed via a Quadratic Programming (QP) torque allocator featuring anti-windup mechanisms and explicit saturation handling to compensate for lateral drift during μ-split braking. Validation is conducted through a Model- in-the-Loop (MIL) to Software-in-the-Loop (SIL) pipeline using scenarios including wet surfaces and panic braking. Simulation results demonstrate enhanced yaw stability and controlled deceleration profiles compared to conventional baselines, ensuring computational feasibility for automotive Electronic Control Units (ECUs).
The current document is a part of an effort of the Active Safety Systems Committee, Active Safety Systems Sensors Task Force whose objectives are to: a Identify the functionality and performance you could expect from active safety sensors b Establish a basic understanding of how sensors work c Establish a basic understanding of how sensors can be tested d Describe an exemplar set of acceptable requirements and tests associated with each technology e Describe the key requirements/functionality for the test targets f Describe the unique characteristics of the targets or tests This document will cover items (a) and (b).
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.
Why ADAS validation can't be solved with more miles alone. Modern advanced driver assistance systems (ADAS) are expected to operate reliably across an almost limitless range of real-world conditions, including changing weather, low lighting, unpredictable traffic behavior, and sensor noise. Validating performance across that level of variability has become one of the most demanding parts of ADAS development. Physical road testing, or even large-scale simulation, alone cannot provide sufficient coverage to meet these demands. This challenge is driven by increasing system complexity. Modern ADAS platforms rely on machine-learning-based perception, multi-sensor fusion, and tightly integrated software architectures that must interpret complex sensor data in real time. Each additional sensing modality, software update, or feature expansion drives a significant validation effort and, in many cases, increases the number of scenarios that must be evaluated.
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.
Active collision avoidance methods are crucial components of vehicle active safety systems, which can effectively prevent collisions or mitigate collision-induced losses. To address the limitations of existing methods, particularly their insufficient foresight in dynamic traffic environments, this paper proposes an active collision avoidance control method based on driving intention recognition and an improved Driving Safety Field (DSF) model to enable more proactive and stable collision avoidance. First, a Hidden Markov Model (HMM) is trained using vehicle trajectory data from a public dataset to accurately identify the driving intentions of the obstacle vehicles, including Lane Change Left (LCL), Lane Keeping (LK), and Lane Change Right (LCR). Then, an improved potential field model is established, which incorporates vehicle acceleration to more comprehensively quantify the driving risk faced by the host vehicle within the DSF model framework. Subsequently, an active collision avoidance controller combining a longitudinal dual-PID braking controller and a lateral MPC steering controller is designed. This controller initiates corresponding avoidance actions based on the results of driving intention recognition and driving risk evaluation. Finally, co-simulation and hardware-in-the-loop (HIL) tests are conducted. The results demonstrate that, compared to conventional methods lacking driving intention recognition, the proposed method can initiate avoidance maneuvers approximately 1 s earlier, thereby more effectively avoiding potential collisions. Furthermore, key vehicle stability indicators, such as lateral acceleration and yaw rate, are significantly reduced during the avoidance process, indicating enhanced stability and satisfactory real-time performance. This method provides a solution for enhancing the active safety of vehicles in complex real traffic scenarios.
Letter from the Guest Editor
Building a trusted digital twin and decision-centric simulation ecosystem The automotive industry has been experiencing significant change and transformation. Electrification, software-defined vehicles, advanced driver assistance systems, and increasing electrical system integration are fundamentally reshaping how vehicles are designed and validated. As integration complexity continues to increase, the expectations for design cycle times are being compressed. Programs that once relied on extended validation timelines are now expected to deliver the same level of confidence in a fraction of the time. Traditional engineering workflows were built around sequential design phases, iterative simulations, and heavy reliance on physical validation. Design concepts were documented, prototypes were constructed, tests were performed, and results were compiled in reports and specifications that informed the next iteration. That approach worked well when systems were less complex and product life cycles were longer. In recent years, the volume of data, the speed of development, and the interconnected nature of modern vehicle architectures demand a different approach.
Edge detection is fundamental for intelligent vehicle applications, directly supporting ADAS functions such as lane detection, obstacle recognition, and scene understanding. The conventional Canny edge detection method exhibits notable shortcomings, especially in color-image processing, adaptive threshold selection, and preserving edge integrity under noisy conditions. In this study, we present an enhanced Canny edge detection framework tailored for ADAS-oriented intelligent vehicle systems, incorporating a quaternion-based weighted averaging scheme for color preservation, adaptive thresholds derived from gradient-amplitude histograms, multiscale edge localization via scale multiplication, and a novel gravitational-field-intensity operator for improved gradient robustness. Moreover, we extend the method to vanishing-point estimation an essential ADAS capability by performing precise intersection calculations combined with clustering techniques such as DBSCAN and RANSAC. Experimental evaluations demonstrate that the proposed algorithm markedly outperforms traditional approaches in edge clarity, localization accuracy, and noise resilience, underscoring its promise for strengthening ADAS perception modules in intelligent vehicles.
Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages—sensor acquisition, video encoding, data transmission, decoding, and display—undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software boundaries. Evaluations demonstrate a latency reduction from a baseline of 45.3 milliseconds to an optimized 23.5 milliseconds, representing a 48.1% improvement without sacrificing spatial resolution or detection robustness. Under optimized configurations, the framework sustains frame rates above 60 FPS at both Full HD and 4K resolutions, with frame drop rates held to approximately 3%. Perceptual evaluation further confirms that object detection accuracy consistently exceeds 91% within the <35 ms latency range, while collision-prediction delays are reduced to below 12.4 ms, ensuring timely responses in dynamic scenarios. These improvements collectively validate the critical importance of hardware-software co-design for embedded vision systems. The results highlight that ultra-low-latency perception is achievable on edge platforms when pipelines are designed with cross-layer optimization, bridging sensor interfaces, video codecs, network transport, and GPU computation. The proposed architecture provides a scalable foundation for future embedded vision deployments in autonomous driving, robotics, and unmanned aerial systems, where low latency is a non-negotiable requirement for safety, reliability, and operational efficiency.
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