EVs Must Pay Their Fair Share

Proposed $250 Federal EV Registration Fee: Fair Share or Political Ploy?

Two guys stroll up to a bar. One’s clean-cut, suit pressed, looking like he just stepped out of a cologne ad, while the other guy’s a mess, wearing stained sweats, reeking of smoke and body odor, dirt smudged on his face, and a cigarette dangling from his face. The doorman eyes them both, smirks, and says to the clean guy, “That’ll be $25.” Then he turns to the mess and goes, “You’re good for $10.” The clean guy sputters, “What?! Why am I paying two and a half times more?” The doorman chuckles, “Buddy, you’re too clean, you’ll make everyone else in the bar look bad. We charge extra for that!”

Well, it looks like the federal government has hired this doorman. A new federal proposal imposes a $250 annual registration fee on electric vehicles (EVs), arguing they use roads and should contribute to maintenance like gas-powered cars. The logic seems clear: roads need funding, and EVs (like all vehicles) use the roads, highways, and byways. But is $250 fair, or is it a move to hinder EV adoption? Let’s dive in.

Gas-powered cars fund roads via the federal gas tax, set at 18.4¢ per gallon. The average US driver covers about 13,500 miles annually, and a typical gas car gets roughly 25 miles per gallon. After a little number crunching, this means the average driver pays $99.36 in federal gas tax per year. Compared to the proposed $250 EV fee, EV owners would pay over 2.5 times more than gas car owners for the same road use.

Vehicle Type	Annual Mileage	Fuel/Tax Type	Annual Cost
Gas Car	13,500 miles	Gas Tax (18.4 cents/gal, 25 mpg)	$99.36
Electric Vehicle	13,500 miles	Proposed EV Fee	$250.00

What’s a fair share? Since EVs use roads similarly to gas cars, their contribution should align. The $99.36 gas tax benchmark shows the $250 fee is excessive. A fair EV fee would match gas car contributions, around $100 annually, possibly adjusted for mileage. This ensures equity without penalizing EV drivers.

If either vehicle type should pay more, gas cars should be the ones to pay additional fees because of all the externalities they hoist onto society. EVs deliver community benefits that gas cars can’t match. EVs produce no tailpipe emissions, reducing air pollution and health costs tied to smog and respiratory issues. Studies estimate gas-vehicle-related pollution costs the US billions annually in healthcare and environmental damage. EVs help curb this. Additionally, funds spent on EV “fuel” (e.g., electricity) stays local, bolstering utility companies and renewable energy investments. In contrast, gas money often flows to oil companies and foreign markets, siphoning money from local economies. For all of these reasons and more, EVs deserve fair treatment, not a steep fee. EVs are the hometown heroes.

I didn't buy an EV as an elaborate (and expensive) scheme to avoid road maintenance taxes, but that doesn't mean i want to overpay either.

The $250 fee reeks of political maneuvering to slow EV adoption. It burdens clean, efficient vehicles while dismissing their societal benefits. It is not a fee because the roads need funding; it's a fee for making gas cars look bad. It's charging the clean guy at the bar extra just for looking good.

If this is the right way to raise funds, then let's apply it to every vehicle. Cancel the federal gas tax and make everyone pay the same amount to use the same roads.  

Policymakers should focus on building an infrastructure for the current century, rather than the 1900s. Impeding progress will only put us behind. Sure, EVs must pay their share, but charging them far more than gas cars is neither fair nor forward-looking. It’s a roadblock to a cleaner future. It’s like charging joggers more for park trails if they _don’t_ litter. It’s absurd! EVs need to chip in, but this fee is a pie-in-the-face of progress. Let’s not let gas-guzzling dinosaurs and their cronies drag us back to the Jurassic era.

Waymo is the Hydrogen Solution of Ride Hail

Image by OpenAI

Both hydrogen fuel cell vehicles and Waymo’s autonomous ride-hailing service are technical marvels, yet both stumble on the steep slope of scalability, especially when costs come into play.

Hydrogen fuel cell vehicles, such as Toyota’s Mirai and Hyundai’s Nexo, power electric motors through a clever process: hydrogen stored in high-pressure tanks reacts with oxygen in a fuel cell, generating electricity to power the motor and drive the vehicle, with water vapor as the only byproduct. A full H2 tank is about 5 kilograms, and this delivers a 300 to 400-mile range. Refueling takes just five minutes. Technically, it works beautifully. The Mirai cruises highways and city streets, matching gasoline cars for refueling convenience and outshining them for emissions.

So why aren't we all driving zero-emission H2 vehicles today? Price and scalability are the roadblocks. Building a hydrogen refueling station costs $1 to $2 million, and the US has only about 50, mostly clustered in California. Compare that to 120,000 gas stations or 50,000 EV chargers across the country. Then there’s the fuel. The cost to fill up a fuel cell car in California is around $16.50 per kilogram. A Toyota Mirai with a 5.6kg tank would cost around $92 to fill up. However, Toyota (and others) offer free fuel for the first three years to help bridge the price gap between hydrogen and gasoline. Assuming owners had to pay for the H2, let's compare the cost per mile. For a Toyota Mirai, it's around $0.31 per mile. Compare that to $0.12 per mile of a gas car and H2 as fuel is like paying $9 per gallon for gas. No one wants to do that. This explains why they have to give away years of free fuel just to sell (lease) the things.

Scaling FCVs to millions of users would require thousands of stations and cheaper hydrogen, demanding billions in investment. High costs keep FCVs a niche player, not a mass-market contender.

Now, let’s shift gears to Waymo, Google's Alphabet’s self-driving transportation service. Waymo’s fleet of modified Chrysler Pacificas and Jaguar I-Paces uses LiDAR, radar, and cameras to navigate without a driver. AI processes a 360-degree view, dodging pedestrians and traffic with precision. It works. Waymo’s vehicles log thousands of miles in cities like Phoenix and San Francisco, safely delivering passengers. But, like hydrogen FCVs, scalability is a struggle. A Waymo vehicle with its sensor suite and compute costs around $100,000, though bulk production might trim that. Operating costs pile up fast: maintenance, software updates, and remote human oversight push per-mile costs to $2 to $3, dwarfing the ~$1 of a human-driven Uber ride. Waymo has raised $5.6 billion, yet profitability lags. Each ride is subsidized, and scaling to millions of vehicles means building charging stations, service hubs, and data centers, costing billions more. The tech is solid, but the price tag for widespread adoption rivals the challenge of hydrogen’s infrastructure.

Both Waymo and hydrogen FCVs prove the possibility of the technology. FCVs glide along, emitting only water, while Waymo’s cars steer through chaos without a human hand. Yet both hit the same scalability wall: cost. Hydrogen’s refueling stations and fuel production demand massive upfront investment, just as Waymo’s sensors, operations, and infrastructure do. Neither technology is cheap enough to flood the market. FCVs need a vast network and affordable hydrogen; Waymo needs leaner tech and lower operating costs. At CarsWithCords.net, we admire the ingenuity, but the path to scalability looks daunting. For now, both remain bold experiments, waiting for a cost breakthrough to drive them mainstream.

Lidar or Camera, Which Sensor Will Win for Autonomous Vehicles?

 Autonomous Driving: A Revolution in Motion

Imagine a world where getting around is as easy as tapping an app, no matter who you are. When autonomous vehicles hit the mainstream, they’ll rewrite the rules of personal mobility. For the blind or elderly, who often face the isolation of being homebound, self-driving cars promise freedom. No longer will a lack of driving ability mean missing doctor’s appointments, social gatherings, or simple errands. These vehicles will be like loyal chauffeurs, ready to whisk anyone, anywhere, safely and reliably. This isn’t just convenience; it’s a lifeline to independence.

Tesla wants to be a primary transportation provider with their Robotaxi service, launching in June of this year. What's different about Tesla's implementation is that it is camera-based and could scale very quickly. Unlike competitors leaning on pricey, specialized sensors like LiDAR, Tesla bets on common cameras as its primary eyes. Vehicles with LiDAR and trunk-sized datacenters might work in select test zones, but they’re like gourmet dishes only available at a few elite restaurants. Tesla’s approach is the food truck of autonomy: affordable, adaptable, and ready to serve the masses. Scalability matters if we want self-driving cars everywhere, not just in elite regions.

I received some feedback on the last article saying that LiDAR was a must-have for safe autonomous driving, but let’s debunk that. LiDAR (LIght Detection And Ranging) uses laser pulses to map surroundings. It is very useful in many applications, but it also has serious blind spots. LiDAR cannot see the color of traffic lights or detect when another car’s brake lights are on, both of which are critical for safe driving. LiDAR struggles in bad weather, as rain, snow, or fog scatter the laser pulses, clouding its view. Plus, LiDAR is expensive, driving up costs for vehicles; affordability is required for a mass-scale solution.

LiDAR-based systems use cameras to catch what the lasers miss, like traffic light colors. That's right, all autonomous vehicles (LiDAR or not) use cameras. However, having both LiDAR and camera input leads to the sensor fusion problem. Sensor fusion is the artful science of blending data from multiple sensors. Combining these different sensor streams creates a problem: What happens when sensors disagree? If one sensor claims it’s raining while another warns that you're heading towards a brick wall at 70 MPH, which do you trust? This clash can confuse the driving AI, causing the AI to make poor or dangerous decisions. Tesla’s camera-only approach sidesteps this by providing one consistent data stream to the AI’s path planning and decision-making processes.

Autonomous vehicle (AV) cameras often sense beyond the human-visible light spectrum, which spans ~400 to 700 nanometers. Many AV cameras also detect shortwave length infrared light, from 700 up to 1100 nanometers, allowing them to see in the dark for improved night vision. This gives AV cameras an edge over human eyes. At night, these cameras excel, capturing clear images in near darkness.

Because AV cameras cover a spectrum of light frequencies, including visible and shortwave infrared, they have an advantage over LiDAR's single laser frequency. The broader spectrum of the cameras allows them to capture diverse visual data. In a way, cameras are already receiving multispectrum data that's "pre-fused" to a single video stream. LiDAR, on the other hand, operates at a fixed frequency (typically 905 nm or 1550 nm). LiDAR excels at depth mapping but misses color and texture details.

The question is, will Tesla's camera-based FSD be safe enough? Crashes will happen at some point. Every AV program of note has had incidents. A fatal crash ended Uber's self-driving program. We'll see if Tesla can navigate this rubric.

Autonomous driving is poised to transform lives, especially for those sidelined by mobility challenges. Tesla’s scalable, camera-driven solution is like that trusty food truck, bringing freedom to every corner, not just the fancy neighborhoods. By avoiding LiDAR’s limitations and the ambiguity of sensor fusion, Tesla is cooking up a future where self-driving cars will be as common as smartphones. This isn’t just about getting from A to B; it’s about giving everyone a ticket to ride, no matter their circumstances.

Disclosure: I am long Tesla