Speed bumps and potholes are the most common road anomalies, significantly affecting ride comfort and vehicle stability. Preview-based suspension control mitigates their impact by detecting such irregularities in advance and adjusting suspension parameters proactively. Accurate and real-time detection is essential, but embedded deployment is constrained by limited computational resources and the small size of targets in input images.To address these challenges, this paper proposes SBP-YOLO, an efficient detection framework for speed bumps and potholes in embedded systems. Built upon YOLOv11n, it integrates GhostConv and VoVGSCSPC modules in the backbone and neck to reduce computation while enhancing multi-scale semantic features. A P2-level branch improves small-object detection, and a lightweight and efficient detection head (LEDH) maintains accuracy with minimal overhead. A hybrid training strategy further enhances robustness under varying road and environmental conditions, combining NWD loss, BCKD knowledge distillation, and Albumentations-based augmentation. Experiments show that SBP-YOLO achieves 87.0% mAP, outperforming the YOLOv11n baseline by 5.8%. After TensorRT FP16 quantization, it runs at 139.5 FPS on Jetson AGX Xavier, yielding a 12.4% speedup over the P2-enhanced YOLOv11. These results demonstrate the framework's suitability for fast, low-latency road condition perception in embedded suspension control systems.

If, like me, you suffer from motion sickness, then you know just how quickly a trip down Britain's winding back roads can turn into a nausea-inducing nightmare. But if you struggle to hold on to your lunch as the car starts to lurch, there may soon be a solution. ClearMotion, a Boston-based startup, claims that its latest generation of cutting-edge suspension can'eliminate motion sickness' for good. So, with anti-nausea tablets in hand, MailOnline's reporter, Wiliam Hunter, took a trip to their Warwickshire testing facility to try it for himself. With compact motors tucked away above each wheel and a sophisticated onboard computer, the system can push and pull the wheels to cancel out bumps in the road.

Road anomalies can be defined as irregularities on the road surface or in the surface itself. Some may be intentional (such as speedbumps), accidental (such as materials falling off a truck), or the result of roads' excessive use or low or no maintenance, such as potholes. Despite their varying origins, these irregularities often harm vehicles substantially. Speed bumps are intentionally placed for safety but are dangerous due to their non-standard shape, size, and lack of proper markings. Potholes are unintentional and can also cause severe damage. To address the detection of these anomalies, we need an automated road monitoring system. Today, various systems exist that use visual information to track these anomalies. Still, due to poor lighting conditions and improper or missing markings, they may go undetected and have severe consequences for public transport, automated vehicles, etc. In this paper, the Enhanced Temporal-BiLSTM Network (ETLNet) is introduced as a novel approach that integrates two Temporal Convolutional Network (TCN) layers with a Bidirectional Long Short-Term Memory (BiLSTM) layer. This combination is tailored to detect anomalies effectively irrespective of lighting conditions, as it depends not on visuals but smartphone inertial sensor data. Our methodology employs accelerometer and gyroscope sensors, typically in smartphones, to gather data on road conditions. Empirical evaluations demonstrate that the ETLNet model maintains an F1-score for detecting speed bumps of 99.3%. The ETLNet model's robustness and efficiency significantly advance automated road surface monitoring technologies.

This paper proposes an enhancement to the ORB-SLAM3 algorithm, tailored for applications on rugged road surfaces. Our improved algorithm adeptly combines feature point matching with optical flow methods, capitalizing on the high robustness of optical flow in complex terrains and the high precision of feature points on smooth surfaces. By refining the inter-frame matching logic of ORB-SLAM3, we have addressed the issue of frame matching loss on uneven roads. To prevent a decrease in accuracy, an adaptive matching mechanism has been incorporated, which increases the reliance on optical flow points during periods of high vibration, thereby effectively maintaining SLAM precision. Furthermore, due to the scarcity of multi-sensor datasets suitable for environments with bumpy roads or speed bumps, we have collected LiDAR and camera data from such settings. Our enhanced algorithm, ORB-SLAM3AB, was then benchmarked against several advanced open-source SLAM algorithms that rely solely on laser or visual data. Through the analysis of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) metrics, our results demonstrate that ORB-SLAM3AB achieves superior robustness and accuracy on rugged road surfaces.

We design and train machine learning models to capture the nonlinear interactions between financial market dynamics and high-frequency trading (HFT) activity. In doing so, we introduce new metrics to identify liquidity-demanding and -supplying HFT strategies. Both types of HFT strategies increase activity in response to information events and decrease it when trading speed is restricted, with liquidity-supplying strategies demonstrating greater responsiveness. Liquidity-demanding HFT is positively linked with latency arbitrage opportunities, whereas liquidity-supplying HFT is negatively related, aligning with theoretical expectations. Our metrics have implications for understanding the information production process in financial markets.

Intel's Core i9-14900K still offers some of the best performance around -- albeit at a similarly beastly power draw -- but offers negligible performance improvement over its direct predecessor, the 13900K. New support for Wi-Fi 7, Thunderbolt 5, and performance-boosting AI features are a nice touch, though. A new generation of refreshed Raptor Lake processors have arrived. After months of rumors and leaks--and an official announcement just yesterday--Intel's latest batch of desktop CPUs take their place as the 14th generation in the Core lineup. You can catch up on the specs and speeds in our comprehensive coverage of the unveiling, but the basics are straightforward. Six new chips have launched, with two variants each of unlocked Core i9, Core i7, and Core i5 parts.

Self-driving cars are hitting city streets like never before. In August the California Public Utilities Commission (CPUC) granted two companies, Cruise and Waymo, permits to run fleets of driverless robo taxis 24/7 in San Francisco and to charge passengers fares for those rides. This was just the latest in a series of green lights that have allowed progressively more leeway for autonomous vehicles (AVs) in the city in recent years. Almost immediately, widely publicized accounts emerged of Cruise vehicles behaving erratically. One blocked the road outside a large music festival, another got stuck in wet concrete and another even collided with a fire truck.

Think your AI understands the meanings of words? Or understands anything at all? Guess again. There's a big issue inherent in trying to make artificial minds that understand like a human does. It's called the Symbol Grounding Problem1S. TLDR: How can understanding in an AI be made intrinsic to the system, rather than just parasitic on the meanings in the minds of the developers / trainers?

Lemonade's recent glitch sheds light on public fears about AI -- and about what must be done to keep AI innovation from slowing. Being a disruptor is hard. It requires taking disproportionate risks, pushing the status quo and -- more often than not -- hitting speed bumps. Recently, Lemonade hit a speed bump in their journey as a visible disruptor and innovator in the insurance industry. I am not privy to any details or knowledge about the case or what Lemonade is or isn't doing, but the Twitter event and public dialogue that built up to this moment brings forward some reflections and opportunities every carrier should pause to consider.

Tesla has always had a unique approach towards self-driving cars. The electric car company has been developing Computer Vision and Synthetic Neural Networks to solve the challenges associated with self-driving cars. While industry giants like Toyota, Google, Uber, Ford and General Motors all have been working with Lidar, Tesla has always proclaimed that Lidar will never be the approach they solve this problem. Founder Elon Musk famously said, "Lidar is a fool's errand, and anyone relying on Lidar is doomed". But what exactly is Lidar's flaw and computer vision's most considerable edge?