Beyond Full Self-Driving: The Smarter, Faster Path to Safer Transit

Beyond Full Self-Driving: The Smarter, Faster Path to Safer Transit

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Introduction

The race to Full Self-Driving (FSD) has become one of the most ambitious and elusive frontiers in AI and mobility. But what if the smartest way forward isn’t to leap into full autonomy—but to augment human drivers in structured systems like buses, trains, and last-mile EVs? Advanced Assisted Driving (AAD), when strategically deployed across electric public transport networks and integrated with unified ticketing, may not only be easier to achieve technically and politically—it could actually move cities toward smarter, safer, and more accessible transportation much faster than waiting for Level 5 autonomy.

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The Problem with Full Self Driving (FSD)

FSD aims to eliminate the human from the driving loop entirely. While this is appealing in theory, it faces:

• Edge-case complexity (weather, pedestrians, unpredictable road behaviors)

• Regulatory uncertainty

• Massive data and compute demands

• Public trust and liability concerns

Most critically, FSD attempts to solve all problems at once—urban, rural, chaotic, structured. This universalism becomes its bottleneck.

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AAD: A Smarter Interim Step

Advanced Assisted Driving doesn’t seek to replace the driver—it empowers them. In structured environments like electric buses and trains (which operate on predefined routes), or even electric last-mile cars (in low-speed urban zones), AAD can provide:

• Collision avoidance

• Lane discipline

• Speed and braking automation

• Fatigue monitoring and alertness support

• Route guidance and schedule optimization

This “pilot + autopilot” model significantly boosts safety and efficiency—without needing to crack the hardest problems of FSD.

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Why Public Transport Is the Ideal Sandbox

Unlike private vehicles, electric buses and trains operate in constrained and predictable environments:

• Defined stops, lanes, and schedules

• Centralized control and fleet management

• Professional drivers trained to collaborate with assistive tech

Integrating AAD here is not only easier to test and scale, it sets a public-sector precedent for AI adoption that benefits society at large.

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Electric Last-Mile Cars: The Missing Link

In dense cities, the last mile is often the slowest, least organized leg of a journey. Deploying electric last-mile vehicles (mini shuttles, pods, tuk-tuk-like EVs) with AAD makes urban mobility safer, smoother, and greener.

These vehicles can be:

• Geo-fenced

• Low-speed (under 30 km/h)

• Easily routed via apps

Such constraints reduce the need for complex AI decision-making while still offering immense benefits in traffic management and user convenience.

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The Power of One Unified Ticket

The final transformative piece is ticket integration. Imagine going from Point A to B using:

• A metro or train for your main leg

• An electric bus to get to your stop

• A last-mile EV car to your doorstep

All with one app, one ticket, one price.

By linking physical mobility with digital unification, the system becomes:

• Easier to use

• Easier to plan

• Easier to fund

• Easier to optimize using data

This creates “intelligent intermodality”: where the system, not just the vehicle, is smart.

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Why This Is a Better Near-Term Bet

Compared to FSD, this model:

• Requires less radical regulatory change

• Delivers real safety benefits now

• Enables public-private collaboration

• Creates sustainable urban mobility with net-zero goals

• Builds public trust in AI transportation systems gradually

In short: AAD for structured electric transport is not just more achievable—it’s more impactful.

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Conclusion

The dream of Full Self Driving may still take another decade—or more. But Advanced Assisted Driving for electric public and last-mile vehicles, linked by unified ticketing, is a future we can build today. It’s not only technologically practical but also aligned with urban planning, public safety, climate goals, and the immediate needs of millions.

Rather than chasing a moonshot, this is a skybridge—connecting where we are with where we need to go.

🚍🧠 Why Advanced Assisted Driving (AAD) might beat Full Self-Driving (FSD) to the future of urban mobility. A thread 🧵

— Paramendra Kumar Bhagat (@paramendra) June 8, 2025

pic.twitter.com/YrOMAT0JdJ

— Paramendra Kumar Bhagat (@paramendra) June 8, 2025

Waymo, Tesla Robotaxi, Cost Per Mile, And Public Transit

 Let’s break it down with real numbers.

🟢 PART 1: Robotaxi vs Human-Driven Taxi (Cost Per Mile)

Let’s take an average human-driven taxi and compare it to a Waymo or Tesla robotaxi:

Assumptions:

Item	Human Taxi	Robotaxi (Electric)
Driver Salary (inc. benefits)	$0.75/mile	$0
Fuel Cost	$0.20/mile	$0.04/mile (electricity)
Maintenance	$0.10/mile	$0.06/mile
Insurance & Licensing	$0.15/mile	$0.15/mile
Vehicle Depreciation	$0.25/mile	$0.25/mile
Total Cost per Mile	$1.45	$0.50

✅ Savings per mile: $0.95 (65%)

So, a robotaxi is 65% cheaper per mile than a human-driven taxi — driven largely by removing the driver and lowering energy costs.

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🟢 PART 2: Autonomous Bus vs Human-Driven Bus

Now let’s scale up to a self-driving electric bus.

Assumptions for 40-seater bus:

Item	Human Bus	Self-Driving Electric Bus
Driver Salary (loaded)	$0.40/passenger-mile	$0
Fuel (Diesel vs Electric)	$0.15/passenger-mile	$0.03
Maintenance	$0.05	$0.03
Insurance & Misc.	$0.07	$0.07
Vehicle Cost (amortized)	$0.13	$0.13
Total Cost per Passenger-Mile	$0.80	$0.26

✅ Savings per passenger-mile: $0.54 (67.5%)

That’s massive. Cities could reduce costs dramatically — from $0.80 to $0.26 per passenger-mile.

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🟢 PART 3: What This Means for Free Public Transit

Let’s do a city-wide calculation:

• Say a city runs 10 million passenger-miles per day.

• Current Cost (Human Bus): 10M x $0.80 = $8M/day

• Autonomous Electric Bus: 10M x $0.26 = $2.6M/day

💸 Daily savings = $5.4M → That’s almost $2 billion/year in savings.

So with enough scale, it may actually be cheaper for cities to run free autonomous electric bus systems than to operate or subsidize current systems. Free, frequent, clean — and automated.

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🟢 Bottom Line

• 🚖 Robotaxis slash 65%+ of costs vs regular taxis.

• 🚌 Self-driving buses cut public transit costs by two-thirds.

• 📉 Removing drivers + switching to electric = huge compounding savings.

• 💡 At scale, free transit isn’t just utopian — it’s fiscally smart.

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Why Surface-Level Smart Public Transit Beats Tunnels and Air Taxis for Dense Cities

Major breakthrough by @BoringCompany! https://t.co/pErVTCDLLV

— gorklon rust (@elonmusk) May 13, 2025

Why Surface-Level Smart Public Transit Beats Tunnels and Air Taxis for Dense Cities

In the race to solve urban mobility challenges, it's tempting to dream big: underground tunnels whizzing people beneath traffic, or air taxis zipping across the skyline. These futuristic visions dominate headlines, promising to "disrupt" how we move in cities. But let’s be clear—if the goal is to maximize traveler density per mile in already densely populated areas, the most efficient, scalable, and humane solution isn’t below or above—it’s right in front of us.

Surface-level smart public transportation is the answer.

The Tunnel Mirage

Elon Musk's tunnel concept, The Boring Company, proposes underground highways to bypass urban congestion. But beyond the significant engineering challenges and costs, there's a hidden toll: psychological discomfort. Being underground, often in confined vehicles with no natural light or orientation, is disorienting and stressful. Not everyone will choose that daily.

Moreover, tunnels are point-to-point, inflexible systems. Adding new stops or changing routes is almost impossible once infrastructure is built. And let’s not forget: underground spaces are inaccessible in emergencies, costly to maintain, and environmentally dubious when compared to surface alternatives.

Air Taxis: Fantasy in the Sky

Air taxis make for great science fiction and VC decks. But they come with loud noise, high energy use, intense safety requirements, and limited carrying capacity. The technology might mature, but it’s unlikely to ever serve more than a niche of high-income travelers.

Even if they become silent, safe, and semi-affordable, air space is limited, and the urban sky simply can’t scale to the density of footpaths, let alone roads or rail lines. They might be part of the mix, but they won't carry the bulk of a city's travelers.

The Smart Surface Revolution

What works is what already works—buses, trains, and taxis—but with a layer of intelligence.

Imagine a city where trains, buses, and last-mile shuttles (or even ride-hailing cabs) are seamlessly connected in a digital ecosystem. A traveler books a journey from Point A to Point B on one app, and behind the scenes, the system calculates the most efficient combo of transport modes. Your train, your connecting bus, your final mile tuk-tuk—all aware of each other’s location, capacity, and timing. No wait times. No gaps in the journey.

This is multi-modal transport, unified through AI and real-time data.

It offers:

• High traveler density per mile at low marginal cost.

• Psychological comfort—open skies, familiar environments, human scale.

• Rapid scalability—you don’t need to dig or fly, just coordinate better.

• Inclusivity—everyone, not just the wealthy, can afford and access it.

Conclusion: Futuristic Doesn’t Mean Floating

Cities don’t need to float in the sky or tunnel like moles to be efficient. The best systems are those that align with human behavior, economic reality, and existing infrastructure.

Yes, explore air taxis and tunnel tech. But don’t lose sight of the real future: a surface-level, intelligent, connected public transit network that feels as smooth as flying, without leaving the ground.

Urban mobility doesn’t need to reinvent physics—it just needs to talk to itself.

Why Surface-Level Smart Public Transit Beats Tunnels and Air Taxis for Dense Cities https://t.co/lakP9pHCXO

— Paramendra Kumar Bhagat (@paramendra) May 13, 2025

Rethinking Self-Driving Cars: The Smarter Future is Seamless Public Transportation

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Rethinking Self-Driving Cars: The Smarter Future is Seamless Public Transportation

Self-driving cars are often heralded as the future of transportation — sleek, autonomous vehicles whisking us from Point A to Point B without the hassle of driving. But step back for a moment, and you realize: self-driving cars are, in some ways, just a flashy rebrand of an old idea — personalized transport — layered on top of an already inefficient system.

Transportation economics teaches us a basic truth: the bigger the vehicle and the more passengers it carries, the cheaper it is per person. Ships on water move goods far more cheaply than trains, which in turn move goods more cheaply than trucks. Similarly, buses — large, shared, and efficient — cost less per person than individual cars, self-driving or not.

The criticism against buses is that they don’t go precisely from your doorstep to your destination. But that “last few miles” problem is precisely where intelligent integration comes into play. Instead of trying to create self-driving cars that do the entire journey — an immensely complex and expensive task — why not combine the strengths of public transportation and personal vehicles into a seamless, smarter system?

Imagine this future:

• You buy one ticket from your true starting point to your final destination.

• Public electric buses, running established routes (easy for autonomous systems to handle), do the heavy lifting across major corridors.

• Self-driving cabs — or even human-driven ones for a long transitional period — meet you at your bus stop for the last few miles.

• Everything talks to each other behind the scenes. The handoff is automatic. You don’t even notice it happening.

Technologically, this is much more achievable. Self-driving buses are a far easier engineering problem than self-driving cars. A bus that runs the same fixed route over and over again can be equipped with a narrower, safer, and more easily trainable AI system. Routes can be pre-mapped with precision, road conditions can be monitored centrally, and predictable traffic flows make the AI’s job much simpler.

Meanwhile, letting cabs handle the last-mile problem — paid out of your single public transport ticket — creates a hybrid system where flexibility meets efficiency. No insisting that one mode of transportation has to solve all problems end-to-end. Instead, each mode does what it’s best at.

The result?

• Lower costs — Energy and operational costs drop dramatically.

• Higher reliability — Dedicated lanes and intelligent coordination reduce traffic snarls.

• Lower emissions — Electric buses and cabs shrink the carbon footprint.

• Faster implementation — We stop trying to crack the hardest nut first (full self-driving on unpredictable urban streets) and instead layer smartness over systems that already work.

If we’re serious about the future of transportation, we need to shift our focus from "self-driving car for every person" to "seamless, smart, shared mobility." High-speed bullet trains city-to-city, electric buses in-city, and cabs for the last mile — this combination is not only more sustainable, but also the most energy- and cost-efficient model available.

The real innovation isn’t just about creating smarter cars — it’s about creating smarter systems.

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