The future of autonomous trucking has arrived! Torc Robotics unveiled its next generation autonomous Freightliner from Daimler Truck North America, powered by Aeva 4D LiDAR. Don't miss the chance to see it this week at ACT Expo / ACT News in Las Vegas!
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In a groundbreaking move for autonomous commercial vehicle technology, Daimler Truck has partnered with Aeva to integrate advanced 4D Lidar into its autonomous trucking program. They are equipping their trucks with a combination of sensor sets, including Lidar, Radar, and Cameras, enabling safe handling in various scenarios, from highways to controlled intersections. This approach highlights the importance of multiple sensor technologies in achieving comprehensive and safe autonomous trucking solutions. How do you see this collaboration influencing the broader landscape of autonomous vehicles? #AutomotiveIndustry #AutonmousVehicles #Technology https://lnkd.in/ex-Wt-_Q
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🔍 Why Camera and Lidar for autonomous trucks? The synergy between cameras and LiDAR offers a comprehensive perception system, ensuring our autonomous trucks navigate with unprecedented accuracy and safety. 🚛 Enhancing Efficiency and Safety: The integration of these technologies isn't just about getting from point A to B. It's about doing so efficiently and safely. With camera and LiDAR working in harmony, our autonomous trucks can: -Navigate through complex environments with ease. -Adapt to changing road conditions swiftly -Enhance fuel efficiency through optimized routes. -Minimize human error, leading to fewer accidents. Want to learn more about simulation, check out this course : https://lnkd.in/dqsPyM_Q
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𝗟𝗶𝗗𝗔𝗥 𝗶𝗻 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗩𝗲𝗵𝗶𝗰𝗹𝗲𝘀 In the realm of autonomous vehicles, LiDAR is a cornerstone technology. It helps these vehicles 'see' their environment by creating a detailed 3D map. This map is crucial for navigation, obstacle avoidance, and path planning, ensuring safety and efficiency on the road. #AutonomousVehicles #LiDARInnovation
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How do autonomous vehicles “see”? 👀 Autonomous vehicles, like FERNRIDE-enabled terminal tractors, are automating horizontal transportation in mixed-traffic terminals. These tractors perceive their environment through cameras, LiDAR, and radar, which gather data to identify objects, track movement, and assess conditions. Autonomous tractors navigate using detailed maps and SLAM technology. However, FERNRIDE's approach also combines advanced perception with human oversight to improve productivity, enhance safety, and provide the highest level of reliability. Read the full article: the link is in the comment section. 🔗👇 #AutonomousVehicles #TerminalOperations #HumanAssistedAutonomy
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Together with Torc Robotics we've selected Aeva to supply advanced 4D LiDAR Technology for series-production Autonomous Trucks. 🚛🤖 ➡ LiDAR is a key perception technology for safe SAE Level 4 autonomous driving. Aeva’s unique Frequency Modulated Continuous Wave (FMCW) 4D LiDAR technology will enable our autonomous-ready Freightliner #Cascadia platform & Torc’s virtual driver to unlock new levels of safety. ➡ We intend to integrate the #LiDAR sensors directly in our production process, making it easy for customers to buy #autonomous ready trucks directly out of our manufacturing plants without the need for sensors to be retrofitted. ➡ The multi-year collaboration begins in the first quarter of 2024 with Aeva’s start of production by 2026 & Daimler Truck production ramp by 2027. Press release: https://dth.ag/Aeva #DaimlerTruck #TORCRobotics #Aeva #AutonomousTrucks
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Ever been in a rush, but that (self-driving) car in front of you is taking it particularly easy? Knowing when and how to safely pass a moving obstacle can be quite the challenge for autonomous vehicles. Former Duckietown students Nikolaj Witting, Fidel Esquivel Estay, Johannes Lienhart, and Paula Wulkop, developed and implemented a solution that enables Duckiebots to detect and maneuver around dynamic as well as static obstacles, while maintaining awareness of oncoming traffic. (Yes, those are two way streets!) Dynamic obstacle avoidance was achieved by enhancing the Duckietown visual lane-following pipeline to detect and navigate around static and moving obstacles. The approach followed was to estimate the pose relative to other Duckiebots based on LED detections, and integrating a dynamic controller node to manage overtaking maneuvers. Want to try and reproduce this at home or for your class? You can start here: https://hubs.la/Q02FPjqN0 #engineeringeducation #robotics #engineering #highereducation #self-drivingcars #math #computervision #robotautonomy #diy #autonomousvehicles #agv #AVs
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Access Services SAE Level 4 accessible autonomous vehicle route mapping at Rancho Los Amigos National Rehabilitation Center... Video Script: Access Services accessible autonomous vehicle(AAV) is scanning the route map with the front LiDars at Rancho Los Amigos National Rehabilitation Center. The upper screen is the vehicle windshield looking forward to the road ahead and the bottom portion of the screen is the monitor to show the digital display of the objects scanned. Video starts with a white van passing from the left side of the AAV in the top screen and the bottom screen shows the van as a moving object toward the north of the screen. The van keeping moving farther away from the AAV and the bottom display reveals the object fades away until it disappeared from the screen. AAV keeps scanning the route at 8 MPH speed. At 38 second on the vedio, an digital object shows at the left side of the monitor from the bottom portion of the screen and no visual object shows on the upper portion of the video screen. This is because the object is behind the front LiDars on the left side of the screen trying to pass the AAV. The LiDar beams has detected the object while the upper video screen shows no visual of the object. The same screen effect continue until at the 48 second on the video, a golf cart object is finally visible to the front video camera and shown in the upper screen. The rest of the video shows the gold cart moving away from the AAV in a much faster speed while the AAV continue scanning the route at 8 MPH speed.
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Our demo vehicle drives autonomously around the table in a figure of eight. Obstacles are detected by the LIDAR and the vehicle immediately swerves to avoid collisions. Localization is performed by our real-time localization system (RTLS), a high-precision IMU and wheel odometry. The algorithms run on a Linux computer with ROS. This solution is used successfully in autonomous vehicles in industry. #ros #robots #autonomous #autonomoussystems #agv #autonomousvehicles #rtls #uwb
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Aligning technology, humans, robots, and business | Chief Customer Officer | Technology, SaaS, RaaS, and Robotics Expert | Startup Advisor | Women in Tech Advocate | Climate Robotics Network Advisor Member
Duckietown in action! Check out this cool research project from our former students. #robotics #robots #autonomous #selfdriving #ai #higherEd #research #womeninrobotics #latinosinrobotics
Ever been in a rush, but that (self-driving) car in front of you is taking it particularly easy? Knowing when and how to safely pass a moving obstacle can be quite the challenge for autonomous vehicles. Former Duckietown students Nikolaj Witting, Fidel Esquivel Estay, Johannes Lienhart, and Paula Wulkop, developed and implemented a solution that enables Duckiebots to detect and maneuver around dynamic as well as static obstacles, while maintaining awareness of oncoming traffic. (Yes, those are two way streets!) Dynamic obstacle avoidance was achieved by enhancing the Duckietown visual lane-following pipeline to detect and navigate around static and moving obstacles. The approach followed was to estimate the pose relative to other Duckiebots based on LED detections, and integrating a dynamic controller node to manage overtaking maneuvers. Want to try and reproduce this at home or for your class? You can start here: https://hubs.la/Q02FPjqN0 #engineeringeducation #robotics #engineering #highereducation #self-drivingcars #math #computervision #robotautonomy #diy #autonomousvehicles #agv #AVs
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