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Episode 85 of Self-Driving Cars with Fexingo dives into a surprisingly tough challenge for autonomous vehicles: the humble roundabout. Lucas and Luna explain why modern roundabouts — with their yield-at-entry, dynamic merging, and multi-lane weaving — create edge cases that confuse even the best sensor stacks. They cite data from Waymo's 2024 Phoenix operations showing roundabout-related disengagements were 3x higher per mile than regular intersections. The hosts also touch on how roundabout design varies globally, from the UK's spiral roundabouts to France's turbo roundabouts, and why no AV company has solved the 'yield vs. stop' ambiguity yet. A specific, well-researched look at a problem most humans don't think twice about.
#AutonomousVehicles #SelfDrivingCars #Roundabouts #Waymo #Lidar #AVChallenges #EdgeCases #TransportationTech #Mobility #Technology #FexingoBusiness #BusinessPodcast #Podcast #AI #MachineLearning #TrafficEngineering #UrbanPlanning #Driverless
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Episode 84 of Self-Driving Cars with Fexingo examines why autonomous vehicles struggle on unpaved roads. Lucas and Luna break down the sensor challenge: gravel, dust, and loose surfaces confuse lidar and camera systems. They reference Wayve's new $8.5 billion valuation and the recent 30% surge in Lucid stock—both tied to autonomy ambitions. The episode uses a real 2024 study showing AV error rates triple on dirt roads. A concrete look at the 'last mile' problem even robotaxis haven't solved.
#SelfDrivingCars #AutonomousVehicles #Lidar #UnpavedRoads #GravelRoads #DirtRoads #Wayve #LucidMotors #Robotaxi #AVTechnology #SensorFusion #EdgeCases #SelfDrivingChallenges #MobilityTech #LucasAndLuna #FexingoBusiness #BusinessPodcast #Technology
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Lucas and Luna drill into one of autonomous vehicles' most stubborn edge cases: why a spinning lidar sensor that can map a smoke-filled room in 3D still goes blind in a snowstorm. They look at the physics of laser wavelengths, the difference between time-of-flight and frequency-modulated continuous-wave lidar, and what the June 2026 market tells us about which companies are betting on which sensor stacks. Specific angle: the trade-off between a 1550-nanometer laser that cuts through fog and a 905-nanometer laser that bounces off every snowflake. Numbers referenced: Tesla's 5-day pop to 420.60, Rivian's 18.5% surge, and the DRIV ETF's modest 2% gain as the market picks winners in the sensor stack debate.
#Lidar #AutonomousVehicles #SelfDrivingCars #SensorTechnology #SnowEdgeCase #Fog #1550nmLaser #905nmLaser #FMCW #TimeOfFlight #Tesla #Rivian #DRIVETF #MobilityTech #Technology #FexingoBusiness #BusinessPodcast #EdgeCases
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Episode 82 of Self-Driving Cars with Fexingo dives into a surprisingly tough edge case for autonomous vehicles: the school bus. Lucas and Luna explore why the distinctive yellow vehicle, with its complex stop-arm signals, flashing lights, and unpredictable child behavior, remains a challenge for lidar and camera systems. They discuss recent testing data from Waymo and the peculiar legal and engineering nuances—like why a bus stopped at a railroad crossing requires different logic than one picking up kids. The episode also touches on how the broader AV industry is approaching these rare but critical scenarios, and why solving school buses matters for public trust.
#SelfDrivingCars #AutonomousVehicles #SchoolBuses #Waymo #Lidar #EdgeCase #AVSafety #MobilityTech #Transportation #AI #MachineLearning #PerceptionSystems #StopArm #FexingoBusiness #BusinessPodcast #Technology #Podcast #June2026
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Lucas and Luna break down a perplexing edge case for autonomous vehicles: the USPS mail truck. Why do AVs struggle with the familiar Grumman LLV, from its unpainted metal surface to its frequent stopping pattern? The episode dives into lidar absorption, sensor fusion failures, and what the aging fleet reveals about the limits of current autonomy. Featuring data on recent AV-related delivery delays and a look at Waymo and Uber's quiet split in Phoenix.
#SelfDrivingCars #AutonomousVehicles #USPS #MailTrucks #Lidar #SensorFusion #Waymo #Uber #Phoenix #EdgeCases #Autonomy #Delivery #Technology #Business #FexingoBusiness #BusinessPodcast #Podcast #AVChallenges
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In this episode, Lucas and Luna tackle a persistent and dangerous edge case for self-driving cars: railroad crossings. With Tesla, Waymo, and others pushing Full Self-Driving and robotaxi services, incidents of AVs stopping on or hesitating at active tracks have surged. Using recent data from the FRA and a specific Waymo incident in San Francisco, they explore why train crossings combine all the worst elements for AV sensors—unusual geometry, variable lighting, metallic interference, and the high stakes of failure. They discuss lidar limitations, the difficulty of predicting train speeds, and why human-like crossing judgment remains elusive. Plus, a look at how automakers like Ford are bringing back veteran engineers to tackle these physical-world challenges that AI alone can't solve. A focused, technical conversation for anyone following the real-world barriers to autonomous mobility.
#SelfDrivingCars #AutonomousVehicles #TrainCrossings #Waymo #TeslaFSD #FordGrayBeards #Lidar #AVSafety #FRA #EdgeCases #Robotaxi #Technology #AutonomousMobility #AI #FexingoBusiness #BusinessPodcast #LucasAndLuna #MobilityTech
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In this episode, Lucas and Luna dive into Ford's surprising move to rehire veteran 'gray beard' engineers after its AI-driven autonomous driving program failed to handle edge cases. They explore why deep learning models struggle with rare road scenarios, how human intuition fills gaps that data can't, and what this means for the broader AV industry. With Tesla FSD under scrutiny and Lucid surging 14.7% in a week, the hosts debate whether the future of self-driving tech lies in hybrid teams of humans and machines. The discussion is anchored by Ford's specific engineering returns and the limitations of current AI training data.
#Ford #GrayBeardEngineers #SelfDrivingCars #AutonomousVehicles #AI #DeepLearning #EdgeCases #AutonomousDriving #TeslaFSD #Lucid #TechCrunch #Technology #Automotive #ArtificialIntelligence #FexingoBusiness #BusinessPodcast #AutonomousTech #Mobility
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Episode 78 of Self-Driving Cars with Fexingo: Autonomous Vehicles, Lidar, and Mobility Tech. Lucas and Luna drill into a specific 2026 bottleneck for autonomous trucks: the 'last mile' problem. While long-haul highway autonomy is operational in Texas and Arizona, getting a fully loaded 40-ton truck to navigate a crowded urban loading dock remains unsolved. We look at the numbers — Aurora Innovation's 2025 commercial launch, TuSimple's pivot to Asia, and the 1.2 million truck-driver shortage that makes this worth solving. Plus: why lidar manufacturers are betting on a 'highway-only' sensor suite to cut costs. Also, a brief listener-support moment from Lucas and Luna.
#SelfDrivingCars #AutonomousTrucks #AuroraInnovation #TuSimple #Lidar #LastMile #Technology #FexingoBusiness #BusinessPodcast #AutonomousVehicles #MobilityTech #Trucking #Logistics #HighwayAutonomy #UrbanDelivery #SupplyChain #DriverShortage #Robotruck
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Episode 77 of Self-Driving Cars with Fexingo tackles a frontier the AV industry has quietly ignored: horses. From Amish country buggies to urban mounted police and trail riders crossing rural roads, equine traffic is unpredictable, skittish, and invisible to most sensor suites. Lucas and Luna dig into why lidar and cameras struggle to classify a horse, how motion patterns differ from cars or pedestrians, and why Tesla's vision-only approach might be especially vulnerable. They cite a 2025 Virginia crash report where an autonomous test vehicle failed to slow for a horse-drawn carriage, and discuss what companies like Waymo and Zoox are (or aren't) doing to solve it. Plus, the hosts reflect on why listener support matters for keeping the podcast ad-free.
#AutonomousVehicles #SelfDrivingCars #Horses #Lidar #Waymo #Zoox #Tesla #AVSafety #RuralRoads #EquineTraffic #Technology #Podcast #FexingoBusiness #BusinessPodcast #SelfDrivingCarsWithFexingo #AI #ComputerVision #SensorFusion
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Episode 76 of Self-Driving Cars with Fexingo tackles a persistent wildlife problem: why autonomous vehicles still can't reliably detect and avoid deer. Lucas and Luna dig into the specific edge cases that trip up sensor fusion and prediction algorithms, from erratic deer movement to low-light conditions near dawn and dusk. They reference recent industry tests showing AVs fail to brake for deer in 30% of scenarios, and discuss why lidar struggles with fur and sudden leaps. The hosts also touch on Tesla's latest vision-only approach versus lidar-heavy competitors like Waymo, and what the data says about real-world collisions. If you've ever wondered why your car's automatic emergency braking works for cars but not for the buck that just ran into the road, this episode explains the gap.
#AutonomousVehicles #SelfDrivingCars #DeerCollisions #AnimalDetection #Lidar #Waymo #Tesla #VisionOnly #EdgeCase #SensorFusion #Wildlife #Technology #FexingoBusiness #BusinessPodcast #Podcast #AVSafety #MachineLearning #Perception
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