Self-Driving Cars with Fexingo: Autonomous Vehicles, Lidar, and Mobility Tech

Self-Driving Cars with Fexingo: Autonomous Vehicles, Lidar, and Mobility Tech

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Self-Driving Cars with Fexingo: Autonomous Vehicles, Lidar, and Mobility Tech episodes

  • Why Self-Driving Cars Still Can't Handle Animal Crossings

    Episode 115 of Self-Driving Cars with Fexingo explores why autonomous vehicles struggle to detect and react to animals on roadways—from deer to livestock to pets. Using a 2025 incident in California where a Waymo swerved to avoid a coyote but rear-ended a parked car, Lucas and Luna unpack the perception and prediction gaps. They discuss lidar's limitations with animal silhouettes, the challenge of training data for rare wildlife encounters, and why even Tesla's vision-only approach has trouble with moose. Market data shows Tesla dropping 4.1% in the last five days while Lucid jumps 16.4%, hinting at shifting investor sentiment. The episode also connects to Uber's $14.8 billion Delivery Hero deal, framing how autonomous delivery vehicles might navigate unpredictable animal crossings. A must-listen for anyone curious about the edge cases still blocking Level 5 autonomy.

    #SelfDrivingCars #AutonomousVehicles #AnimalCrossings #Waymo #Tesla #LucidMotors #Lidar #ComputerVision #EdgeCases #AutonomousDelivery #Uber #DeliveryHero #CoyoteAccident #MooseTesting #PerceptionAI #Technology #FexingoBusiness #BusinessPodcast

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    8 min
  • Why Self-Driving Cars Still Cant Handle Gravel Roads

    Lucas and Luna unpack a persistent blind spot in autonomous vehicle technology: unpaved gravel roads. These surfaces cover millions of miles globally but are nearly invisible in AV training data. The hosts explore why lidar, camera, and radar systems all struggle with loose gravel, dust, and inconsistent terrain. They reference recent data showing Tesla's stock dip after a fatal Texas crash involving gravel-road confusion, and discuss how Waymo, Cruise, and smaller players are approaching the problem. The episode centers on a specific 2024 study from the University of Michigan that found AV perception accuracy drops by over 40% on gravel versus paved roads, and what that means for rural deployment timelines.

    #SelfDrivingCars #AutonomousVehicles #GravelRoads #Lidar #PerceptionSystems #Tesla #Waymo #Cruise #AVChallenges #RuralMobility #UniversityOfMichigan #NTSB #TeslaCrash #Technology #FexingoBusiness #BusinessPodcast #Fexingo #MobilityTech

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    9 min
  • Why Self-Driving Cars Still Get Confused by Construction Zones

    In this episode, Lucas and Luna explore a persistent blind spot in autonomous vehicle perception: construction zones. They break down why temporary lane markings, orange cones, and human flaggers confuse even the most advanced sensor suites. Lucas shares data on how Waymo and Tesla handle (or fail to handle) work zones, and they discuss a July 2026 NTSB report on a Tesla fatality in Texas where full accelerator depression was recorded. They also touch on how lidar and cameras interpret—or misinterpret—common construction materials like reflective tape and jersey barriers. A concrete look at why road work remains a hard problem for self-driving tech.

    #SelfDrivingCars #AutonomousVehicles #ConstructionZones #Lidar #Tesla #Waymo #NTSB #RoadSafety #Technology #MachineLearning #Perception #AutonomousDriving #FexingoBusiness #BusinessPodcast #TechPodcast #AV #SensorFusion #WorkZones

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    8 min
  • The One Problem Self-Driving Cars Still Face with School Zones

    Self-driving cars can handle highways and suburban streets, but school zones remain a uniquely hard problem. Lucas and Luna dive into why autonomous vehicles struggle with flashing beacons, crossing guards, unpredictable kids, and temporary signage. They reference Rivian's 5% recent gain and Lucid's 20% drop as context for the industry's uneven progress. With school zones appearing millions of times across the US, each with slightly different layouts and rules, the edge cases pile up fast. Lucas explains how lidar and cameras both fail on reflective safety vests and slow-moving school bus stop signs. Luna asks whether geofencing or V2X communication could finally solve it. The episode ends with a look at one startup's radar-based approach that might be cheaper and more reliable than lidar in this specific scenario.

    #SelfDrivingCars #AutonomousVehicles #SchoolZones #Lidar #Radar #AVSafety #EdgeCases #Rivian #Lucid #Geofencing #V2X #Sensors #Startup #Technology #Business #FexingoBusiness #BusinessPodcast #AutoTech

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    12 min
  • Why Self-Driving Cars Still Can't Handle Electric Vehicle Fires

    Episode 111 of Self-Driving Cars with Fexingo drills into a specific, urgent edge case: lithium-ion battery fires in electric vehicles. Lucas and Luna explain why thermal runaway is uniquely challenging for autonomous vehicle sensor suites and decision algorithms. Using data from July 14, 2026 — including Lucid Motors' 20.6% weekly drop amid bankruptcy rumors and GM's 0.8% uptick — they connect the technical problem to real market pressure. They walk through how lidar and cameras fail in heavy smoke, why burning EVs require different emergency response protocols than gas cars, and what companies like Tesla and Rivian are (and aren't) doing to solve it. A focused, concrete episode about a niche problem that delays full autonomy deployment.

    #SelfDrivingCars #AutonomousVehicles #ElectricVehicleFires #ThermalRunaway #LidarLimitations #EVSafety #Tesla #Rivian #LucidMotors #GM #FireResponse #AutonomySetbacks #SensorFusion #BatteryTechnology #Technology #EdgeCases #FexingoBusiness #BusinessPodcast

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    10 min
  • Why Self-Driving Cars Still Get Confused by Horse-Drawn Carriages

    In this episode, Lucas and Luna explore a bizarre but persistent edge case for autonomous vehicles: horse-drawn carriages. While self-driving systems have improved dramatically in 2026, they still struggle to interpret the unpredictable movements of large animals and the unique traffic patterns carriages create. Lucas cites a June 2026 report from the Amish Safety Council documenting incidents in Lancaster County, Pennsylvania, where autonomous test fleets have had to disengage 23 times in the past year due to carriage encounters. The discussion covers why lidar and camera systems misinterpret a horse's gait as multiple objects, why neural networks lack training data for buggies, and how companies like Waymo and Aurora are now hiring equestrian consultants to label data. The episode closes with a reflection on whether these edge cases truly matter for the long-term viability of self-driving technology.

    #SelfDrivingCars #AutonomousVehicles #HorseDrawnCarriages #AmishCountry #EdgeCases #Lidar #Waymo #AuroraInnovation #LancasterCounty #NeuralNetworks #RoadSafety #Technology #FexingoBusiness #BusinessPodcast #Fexingo #MobilityTech #AVTesting #PerceptionSystems

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    10 min
  • Why Self-Driving Cars Still Cant Handle Bicycle Lanes

    In this episode of Self-Driving Cars with Fexingo, hosts Lucas and Luna tackle a persistent blind spot for autonomous vehicles: bicycle lanes. Despite years of development, AVs still struggle to interpret the nuanced rules and unpredictable behaviors in bike infrastructure. Lucas drills into a specific case from July 2026 involving a Waymo vehicle in San Francisco that hesitated for over two minutes at a conflict zone where a bike lane merges with a right-turn lane. The conversation covers how lidar and cameras misread cyclists' hand signals, why painted bike lanes confuse semantic segmentation models, and the difference between European protected lanes and American painted lanes. They reference Rivian's recent 5% stock bump tied to its new cyclist-detection patent, and discuss how Tesla's vision-only approach fares worse than lidar-plus-camera systems in low-contrast painted lanes. Luna challenges Lucas on the regulatory gap, noting that no US state mandates AV testing in bike-heavy environments. The episode ends with a forward-looking question about whether dedicated AV-only lanes would solve the problem or just fragment the road further. A brief donation segment around the 75% mark acknowledges the show's ad-free model and directs listeners to buy me a coffee dot com slash fexingo.

    #SelfDrivingCars #AutonomousVehicles #BicycleLanes #Lidar #Waymo #Rivian #Tesla #TransportationTech #CyclistSafety #AVRegulation #ComputerVision #SanFrancisco #TrafficEngineering #MobilityTech #Technology #FexingoBusiness #BusinessPodcast #Podcast

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    8 min
  • Why Self-Driving Cars Still Can't Handle Emergency Vehicles in 2026

    In this episode, Lucas and Luna examine the persistent challenge of first-responder detection in autonomous vehicles. After a recent TechCrunch article about a robotaxi ultimatum, they dig into why fire trucks, ambulances, and police cruisers still confound sensor suites. They discuss the limitations of lidar on light bars, the acoustic detection gap, and Waymo's latest fix. With specific examples from the July 2026 market data—GM and Ford flat while Rivian and Lucid tumble—they ask whether the industry is investing enough in edge cases. The conversation is grounded in concrete numbers and real-world testing data, not hype.

    #SelfDrivingCars #AutonomousVehicles #Lidar #EmergencyVehicles #Robotaxi #Waymo #GM #Ford #Rivian #Lucid #NIO #FexingoBusiness #BusinessPodcast #Technology #AVSafety #FirstResponders #SensorFusion #EdgeCases

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    9 min
  • Why Self-Driving Cars Still Can't Handle Farm Equipment on Public Roads

    In Episode 107 of Self-Driving Cars with Fexingo, Lucas and Luna explore why autonomous vehicles struggle with farm equipment on public roads. Agricultural machinery like combines and tractors move slowly, have irregular shapes, and often kick up dust that confuses lidar and camera systems. Lucas cites a 2024 NHTSA report showing 30 percent of autonomous-vehicle disengagements on rural routes involve farm vehicles, and notes that Rivian and Lucid have both dropped by over 13 percent in the past five days as investors worry about rural deployment timelines. Luna points out that combines can be 40 feet wide with metal augers that lidar reads as a wall of noise. The hosts discuss why Waymo and Cruise avoid farm country entirely, and what it would take for systems to handle a tractor turning left at dusk. A concrete, surprising angle on a corner case the industry rarely talks about.

    #SelfDrivingCars #FexingoBusiness #BusinessPodcast #Technology #AutonomousVehicles #FarmEquipment #Lidar #Rivian #Lucid #NHTSA #RuralRoads #CornerCases #Waymo #Cruise #Tractors #Combines #AVDisengagement #Agriculture

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    8 min
  • Why Self-Driving Cars Still Get Confused by Roundabouts

    Roundabouts — those circular intersections that are safer and more fuel-efficient than traffic lights — remain a surprising challenge for autonomous vehicles in 2026. In this episode, Lucas and Luna break down why self-driving cars struggle with roundabouts: the need to predict human driver intent in dynamic merging, the limitations of current sensor fusion in curved geometries, and why even Waymo's latest system sometimes hesitates or bails out. They also discuss how driver behavior in roundabouts varies by region — and why solving roundabouts may unlock trust for AVs in suburban and European markets. Stock context: Rivian and Lucid are both down double digits over the past week, and autonomous driving progress is a key differentiator in the EV space.

    #SelfDrivingCars #AutonomousVehicles #Roundabouts #Waymo #Cruise #Lidar #SensorFusion #Robotaxis #TrafficEngineering #AVChallenges #Rivian #Lucid #TeslaFSD #MobilityTech #Transportation #Technology #FexingoBusiness #BusinessPodcast

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    10 min

About Self-Driving Cars with Fexingo: Autonomous Vehicles, Lidar, and Mobility Tech

From the publisher's feed

Lucas and Luna dissect the business of autonomous mobility — not the hype, but the unit economics, sensor supply chains, and regulatory timelines that separate viable players from vaporware. Each episode opens with fresh data: lidar sensor prices from Yole Group, NHTSA accident reports, Waymo and Cruise fleet expansion figures, and the latest SPAC filings from mobility-tech startups. Lucas, a journalist covering automotive tech for a decade, presses the numbers: 'Is L4 deployment actually accelerating, or are we just seeing more test miles reported?' Luna, an engineer turned product strategist, matches him with on-the-ground sourcing — what a procurement manager at a Tier 1 supplier told her about LiDAR yield rates, or why a city planner in Austin chose to restrict autonomous delivery bots. Together, they build a grounded picture of who's winning the talent war (former Tesla Autopilot engineers now at Aurora), which chip fabs are securing long-term contracts for radar SoCs, and why insurance premiums for robo-taxis remain a hidden drag on unit economics. They don't forecast a future; they interrogate the present. Can the autonomous-vehicle industry survive a capital winter? When does a prototype become a product? This show is for the investor, engineer, or policy analyst who needs to know where the puck is — not where the press release says it is.