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

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...

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 Innovatio...

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 pedes...

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...

Episode 75 of Self-Driving Cars with Fexingo dives into a persistent blind spot: autonomous vehicles and railroad crossings. Lucas and Luna explore why even the most advanced sensor stacks—lidar, radar, cameras—struggle with active train crossings, from misjudging gate arms to misreading flashing lights. They break down two recent incidents: a Waymo that hesitated at a crossing in Arizona, causing...

Episode 74 of Self-Driving Cars with Fexingo examines one of the most dangerous edge cases for autonomous vehicles: construction workers directing traffic in work zones. Lucas and Luna discuss why even the latest sensor suites struggle with human hand signals, how a recent incident involving a Waymo vehicle in Phoenix exposed the gap, and what companies like Waymo and Aurora are doing to solve it....

Episode 73 of Self-Driving Cars with Fexingo tackles an overlooked challenge for autonomous vehicles: navigating through wildfire smoke. Lucas and Luna break down how particulate matter scatters lidar beams, why camera-based systems lose contrast in haze, and what it means for AVs in the Western US during fire season. With Tesla down 6.3% and the Global X Autonomous & Electric Vehicles ETF losing...

In this episode, we look at the surprising challenge of dust storms for autonomous vehicles. While rain and snow are well-known obstacles, airborne particulate matter from dust can blind lidar and confuse cameras in ways that are harder to predict and model. Lucas shares recent test data from Waymo and WeRide showing how dust causes false positives and degraded sensor performance. Luna points out...

Episode 71 of Self-Driving Cars with Fexingo: Autonomous Vehicles, Lidar, and Mobility Tech explores a persistent edge case: how self-driving car sensors interpret — or fail to interpret — hand signals from police officers directing traffic. With Tesla down over 5% in the past five days and Rivian dropping 10%, the hosts discuss why solving this problem is both a technical challenge and a regulato...

In this episode, Lucas and Luna dig into a persistent blind spot for autonomous vehicles: construction zones. Despite billions in R&D, robotaxis and ADAS systems still struggle with the chaotic, temporary geometry of road work. Lucas cites recent data showing that Waymo's Phoenix fleet disengages at construction sites 3x more often than on standard highways. They discuss why lidar and camera syste...

Episode 69 of Self-Driving Cars with Fexingo dives into one of autonomy's most stubborn weather challenges: fog. Lucas and Luna break down why fog wreaks havoc on lidar, cameras, and radar—and how the industry is fighting back. They reference Waymo's recent testing in San Francisco's summer fog, discuss why traditional sensor fusion breaks down below 50 meters of visibility, and explore promising...

Lucas and Luna explore why snow remains one of the toughest challenges for self-driving cars. They break down the physics: falling snow confuses lidar, snow-covered roads erase lane markings, and salt spray creates false positives. They reference Waymo's limited winter operations in Minnesota, Tesla's struggles with Autopilot in snowy conditions, and the latest lidar improvements from Luminar. The...

In this episode, Lucas and Luna explore why heavy rain remains a stubborn challenge for self-driving cars. They break down how raindrops scatter lidar beams, how camera-based systems lose lane markings, and why even radar struggles with water attenuation. Drawing on recent research from Waymo and the University of Michigan, they explain why the industry's current sensor fusion approach still fails...

Episode 66 of Self-Driving Cars with Fexingo examines China's surprising lead in autonomous ride-hailing. Lucas and Luna break down a recent TechCrunch mobility scorecard that shows Chinese robotaxi fleets—operated by Baidu, Pony.ai, and WeRide—now outnumber U.S. deployments by nearly 3 to 1. They discuss the regulatory and infrastructure factors behind the gap, from city-level AV permits in Beiji...

Episode 65 of Self-Driving Cars with Fexingo dives into a surprisingly stubborn edge case: police hand signals. Lucas and Luna explore why autonomous vehicles struggle to interpret a traffic cop's gestures at intersections, referencing a 2025 study from the University of Michigan that found self-driving systems misidentified hand signals 23% of the time. They discuss the gap between computer visio...

Episode 64 of Self-Driving Cars with Fexingo tackles the edge case that keeps AV engineers up at night: cyclists. Lucas and Luna explore why bikes are uniquely challenging for autonomous systems—unpredictable lane positioning, subtle hand signals, and the dreaded 'dooring' scenario. They reference recent data, including a 12% rise in cyclist fatalities in the US last year, and discuss how companie...

In this episode of Self-Driving Cars with Fexingo, Lucas and Luna dive into a surprisingly stubborn problem: unpaved roads. While autonomous vehicles have made progress on highways and city streets, dirt, gravel, and unpaved rural roads remain a major challenge. Lucas explains why lidar and camera systems struggle with loose surfaces, dust clouds, and inconsistent lane markings. They reference rec...

In this episode, Lucas and Luna explore why potholes remain a stubborn challenge for self-driving cars. They discuss how potholes break the continuous road surface that AV perception systems rely on, forcing cars to treat them as obstacles or make risky real-time maneuvers. The conversation draws on examples from Waymo's Phoenix operations and recent research on pothole detection, while noting how...

Lucas and Luna dive into one of the most stubborn edge cases for self-driving cars: emergency vehicles. With new data from Waymo's 2025 safety report, they break down why flashing lights, sirens, and unpredictable behavior still confuse AV perception systems. Lucas explains the 'siren vortex' phenomenon and how one Arizona fire department saw a 40% increase in response delays in AV-heavy areas. Th...

Episode 60 of Self-Driving Cars with Fexingo tackles a surprisingly tough challenge for autonomous vehicles: navigating parking lots. Lucas and Luna break down the technical hurdles that make parking lots more complex than highways—from irregular layouts and tight spaces to unpredictable pedestrian behavior. They reference Tesla's struggles with valet mode, Waymo's cautious approach, and GM's Crui...

Episode 59 of Self-Driving Cars with Fexingo dives into a surprisingly tough challenge for autonomous vehicles: the drive-through lane. Lucas and Luna explore why fast-food and coffee pickup lanes—with their tight spaces, unpredictable stop-and-go, and reliance on human handoffs—push current AV stacks to their limit. They reference Rivian's recent lawsuit over false self-driving promises and exami...

Episode 58 of Self-Driving Cars with Fexingo examines why autonomous vehicles struggle with school buses. Lucas and Luna break down the specific challenges: the unique visual profile of a yellow bus, the unpredictability of children running, the stop-arm law variations across states, and the complex hand signals from drivers. They reference recent news of a Waymo bus interaction in Arizona and con...

Episode 57 of Self-Driving Cars with Fexingo explores a surprising weakness: why self-driving cars still struggle with traffic lights. Lucas and Luna dive into a recent NHTSA report showing that over 12% of AV disengagements in 2025 were triggered by traffic signal confusion—from misreading green arrows to hesitating at blinking reds. They break down the technical challenges: how current cameras a...

Lucas and Luna explore a persistent blind spot in autonomous vehicle technology: railroad crossings. While self-driving cars have made strides in handling pedestrians, cyclists, and even emergency vehicles, they still struggle with the complexity of grade crossings — from multiple tracks and gates to freight trains that can take minutes to pass. The hosts discuss why lidar and cameras can be foole...

Episode 55 of Self-Driving Cars with Fexingo dives into Mobileye's unusual robotaxi strategy in the US. Unlike Waymo or Cruise, which operate their own fleets, Mobileye plans to sell its self-driving technology to multiple ride-hail operators while also running a small fleet itself. Lucas and Luna break down the 'both sides' business model, what it means for the autonomous vehicle industry, and ho...