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

Episode 104 of Self-Driving Cars with Fexingo tackles a deceptively tricky edge case: school buses. Lucas explains why autonomous vehicles struggle with the unique combination of irregular stops, flashing lights, and unpredictable kid behavior. Luna points out that even lidar can be fooled by the bus's reflective yellow paint. They reference recent stock moves in Rivian and Lucid to frame the broa...

In this episode, Lucas and Luna explore why autonomous vehicles still struggle with a mundane but critical feature of urban roads: speed bumps. They examine how lidar and camera systems interpret raised pavement, the problem of inconsistent municipal designs, and why even the most advanced sensor suits fail on a standard 12-foot-wide hump. With Tesla down nearly 3% and Rivian dropping 13% this wee...

Episode 102 of Self-Driving Cars with Fexingo dives into a persistent blind spot for autonomous vehicles: wet roads after dark. Lucas and Luna break down why water on asphalt turns lidar into a noise machine, how retroreflectivity tricks sensors, and why Tesla's camera-only approach has an unexpected edge here. They reference recent market moves for lidar stocks like DRIV and IDRV, plus Stellantis...

Episode 101 of Self-Driving Cars with Fexingo digs into a deceptively hard problem: autonomous vehicles in parking lots. While highway driving is increasingly solved, the unstructured chaos of a parking lot — with pedestrians, shopping carts, blind corners, and erratic drivers — remains a massive challenge. Lucas and Luna discuss why lidar and cameras struggle with the 'last 50 feet' of autonomy,...

In this episode of Self-Driving Cars with Fexingo, Lucas and Luna explore why autonomous vehicles still struggle to detect and yield to emergency vehicles like ambulances and fire trucks. They discuss the sensor limitations (lidar vs. siren triangulation), a recent federal demand that AV companies stop blocking first responders, and how Tesla's stock dip reflects market skepticism. The hosts also...

Episode 99 of Self-Driving Cars with Fexingo: why dirt roads remain a nightmare for autonomous vehicles in 2026. Lucas and Luna dive into the specific sensor and mapping challenges posed by loose gravel, dust, and changing terrain profiles. They reference Tesla's recent struggles with unpaved routes and compare approaches from Waymo and Chinese automakers like NIO. The conversation also touches on...

In this episode of Self-Driving Cars with Fexingo, Lucas and Luna dive into one of the most deceptively hard challenges for autonomous vehicles: the unprotected left turn. While highways and right turns have largely been solved, left turns across traffic remain a major bottleneck for robo-taxi deployment in 2026. Lucas breaks down why the problem is fundamentally harder than it looks — it involves...

Episode 97 of Self-Driving Cars with Fexingo tackles a messy problem: why autonomous vehicles can't see when mud, dirt, or road grime covers the sensors. Lucas and Luna break down the physics of lidar attenuation through opaque layers, using real-world testing data from a major AV operator that saw a 40% perception failure rate on muddy vehicles after off-road or construction zone exposure. They d...

Episode 96 of Self-Driving Cars with Fexingo dives into a persistent edge case: flooded roads. Lucas and Luna explain why standing water confuses lidar, cameras, and ultrasonic sensors, using real-world examples like Hurricane Harvey and recent 2026 storms. They discuss how water's reflectivity, refraction, and depth estimation challenges lead to puddles that look like mirrors, depthless voids, or...

In this episode, we explore why autonomous vehicles still struggle to detect and respond to emergency vehicles like fire trucks, ambulances, and police cruisers. Lucas breaks down the sensor limitations — lidar reflections on flashing lights, audio confusion with sirens, and the lack of a standardized V2V protocol for emergency alerts. Luna brings up Rivian's recent 19.8% stock surge amid its new...

Episode 94 of Self-Driving Cars with Fexingo examines a persistent edge case: emergency vehicles. Lucas and Luna break down why autonomous systems struggle to detect sirens, predict erratic paths, and coordinate with human-driven fire trucks — especially at intersections. They reference current market data, including Rivian's recent surge, and connect the technical challenge to broader industry hu...

Episode 93 of Self-Driving Cars with Fexingo dives into one of the weirdest edge cases left in autonomous vehicle perception: glossy black paint. Lucas and Luna explain why lidar beams get absorbed or scattered by dark, shiny surfaces, causing AVs to 'see' a black car as empty space. They cite a 2025 study from MIT's Lincoln Lab showing that standard 905-nanometer lidar loses up to 90 percent of r...

Lucas and Luna dive into a specific pain point for autonomous vehicles in 2026: construction zones. With over 30,000 active highway work zones across the US, AVs face shifting lanes, temporary signals, and unpredictable human flaggers. Lucas explains why the structured highway environment breaks down when a sign says 'right lane closed' in 15 different visual languages. They discuss how Waymo and...

In Episode 91 of Fexingo's Self-Driving Cars, Lucas and Luna tackle a deceptively hard problem: why autonomous vehicles still can't handle automatic car washes. They break down the specific sensor challenges—how soap and water confuse lidar return signals, how spinning brushes disrupt ultrasonic parking sensors, and why reflective wet surfaces fool camera-based lane detection. Lucas connects it to...

Episode 90 of Self-Driving Cars with Fexingo tackles a subtle but costly bottleneck: the final fifty feet of autonomous trucking. Lucas and Luna explain why dock workers flash hand signals that cameras misinterpret, why trailers arrive with mismatched paperwork, and why even a perfect highway run falls apart when a loading bay is blocked by a forklift. The episode uses data from July 2026, includi...

Episode 89 of Self-Driving Cars with Fexingo dives into a surprisingly persistent edge case: toll booths. Autonomous vehicles from Waymo, Cruise, and Tesla still struggle with the precise positioning required for manual toll lanes, cash payments, and transponder alignment. Lucas breaks down the sensor limitations—lidar resolution versus lane width, camera occlusion by toll arms—and Luna notes how...

In episode 88 of Self-Driving Cars with Fexingo, Lucas and Luna explore why autonomous vehicles struggle with gravel roads. They break down the physics of loose surfaces, the limitations of lidar and camera systems on unpaved terrain, and the real-world impact for rural and developing markets. With Rivian up 19 percent in five days and GM and Ford down, the conversation ties current market moves t...

Potholes are a mundane road hazard that autonomous vehicles still struggle to detect and navigate. In this episode, Lucas and Luna examine why computer vision systems fail on broken pavement, how the industry has tried to solve the problem with LIDAR and neural nets, and why the challenge matters more than you think. They reference Rivian's recent 19% stock jump as a signal of investor appetite fo...

Episode 86 of Self-Driving Cars with Fexingo: Autonomous Vehicles, Lidar, and Mobility Tech dives into the specific operational design domain that AVs ace—highways—and the urban intersection problem they still can't crack. Lucas and Luna break down why highway autonomy is a solved engineering challenge, while intersections remain the hardest unsolved edge case. They reference NVIDIA's DRIVE platfo...

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 disengage...

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...

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...

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 engine...

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...

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 w...