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Slide 01
Autonomous
Vehicles
- Future Technologies
- From horsepower to software — the self-driving revolution reshaping mobility
- L0 No Automation
- L2 Partial
- L4 High
- L5 Full
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Slide 02
What Are Autonomous Vehicles?
- Definition
- Autonomous vehicles (AVs) use a combination of sensors, AI, and actuators to navigate and operate with minimal or zero human input — replacing one of the most error-prone tasks in modern civilization: human driving.
- Perception
- Cameras, LiDAR, radar, and ultrasonic sensors building a real-time 3D map of everything within 200m in milliseconds.
- Planning
- AI systems planning optimal routes, predicting other road users' behavior, and making thousands of micro-decisions per second.
- Control
- Precise actuation of steering, throttle, and brakes with millisecond response times and sub-centimeter accuracy.
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Slide 03
SAE Levels of Automation
- Standards
- Level 0 — No automation. All driving tasks performed by human driver.
- Level 1 — Driver assistance. One function (cruise or steering) automated, not both.
- Level 2 — Partial automation. Both steering and acceleration automated; driver monitors at all times.
- Level 3 — Conditional automation. System drives; driver must respond to takeover requests.
- Level 4 — High automation. Can drive without human in defined conditions or geofenced areas.
- Level 5 — Full automation. No human needed in any condition — steering wheel optional.
- Where We Are Today
- L2+: Tesla Autopilot, GM Super Cruise, Ford BlueCruise — millions of vehicles on roads today
- L4 limited: Waymo One robotaxi (Phoenix, SF), Cruise (SF), Baidu Apollo (China)
- L5: Not yet achieved commercially — edge cases remain unsolved by any system
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Slide 04
60 Years to Self-Driving
- History
- 1966
- Stanford Cart
- First computer-vision-guided vehicle navigates a chair-filled room at Stanford over 5 hours.
- 1987
- VaMoRs (Germany)
- Ernst Dickmanns' Mercedes van drives 90 km/h autonomously on public roads in Bavaria — a decade ahead of its time.
- 2005
- DARPA Grand Challenge
- Stanley (Stanford) becomes first autonomous vehicle to complete a 132-mile desert course — winner of $2M prize.
- 2009
- Google Self-Driving Project
- What would become Waymo begins — accumulating millions of public road miles in the following years.
- 2018
- First Commercial Robotaxi
- Waymo One launches the world's first commercial autonomous taxi service in Phoenix, AZ.
- 2023
- California Expansion
- Waymo and Cruise receive permits for 24/7 commercial operations in San Francisco without safety drivers.
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Slide 05
The Sensor Stack
- Technology
- AVs combine multiple sensor types into a redundant, fused perception system — each covering the others' blind spots and failure modes.
- LiDAR — Laser pulses build a precise 3D point cloud of surroundings at 100m+ range; $75 (2024) vs. $75,000 (2010)
- Cameras — 8+ cameras for color, lane markings, traffic signs, and semantic understanding
- Radar — Penetrates rain, fog, snow; measures speed of objects directly via Doppler effect
- Ultrasonic — Short-range proximity sensing for parking, low-speed maneuvering
- HD Maps — Centimeter-accurate maps providing context that sensors alone cannot capture
- V2X Communication — Vehicles sharing real-time data with each other and infrastructure
- Camera-Only: Tesla's Bet
- Tesla controversially removed radar and ultrasonic in 2021–2022, relying entirely on cameras + neural networks — mimicking how humans drive with only vision.
- Full Fusion: Waymo's Approach
- 29 cameras + 5 LiDARs + 6 radars per vehicle — heavily redundant, premium hardware, maximum safety at higher cost.
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Slide 06
The AI Brain
- Perception models — Deep CNNs classifying objects, segmenting scenes, estimating depth, tracking motion across frames
- Prediction models — Forecasting pedestrian and vehicle trajectories 5–10 seconds ahead using behavioral priors
- Planning systems — Generating safe, comfortable, legal trajectories through dynamic environments in real time
- Simulation — Training and testing in virtual environments — Waymo runs billions of simulated miles per year
- Fleet learning — Every mile driven across the fleet updates the shared model — millions of vehicles = massive data advantage
- Onboard compute — Custom AI chips (Tesla FSD, Waymo custom SoC) processing terabytes of sensor data per hour
- Tesla FSD Architecture
- Occupancy networks replace object detection — predicting whether each 3D voxel is occupied vs. free space, enabling driving without explicit object categorization.
- 144 TOPSTesla FSD chip compute power
- 8B+miles Tesla fleet has driven
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Slide 07
The Safety Imperative
- Safety
- AVs must be justified on safety — 1.35 million people die in road crashes annually worldwide, with 94% attributable to human error. This is the technology's moral case.
- Waymo's robotaxis have 6.8× fewer injury-causing crashes than human drivers in comparable conditions
- AV systems don't drink, text, speed, or fall asleep — eliminating the top four causes of road deaths
- Reaction time of 100ms vs. human average of 1,500ms — orders of magnitude faster emergency response
- 360° awareness with no blind spots, regardless of weather conditions or lighting
- Consistent, rules-following behavior eliminates aggressive and distracted driving
- 1.35M
- global road deaths per year
- 94%
- caused by human error
- $1.8T
- annual cost of road crashes in the US
- 6.8×
- safer: Waymo vs. human drivers
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Slide 08
The Race to Full Autonomy
- Players
- Waymo (Google)
- 25M+ autonomous miles in 5 cities. Gold standard in safety data. Raised $5.5B in 2024 to expand to 10 US cities.
- Tesla
- ~6M vehicles with FSD hardware. 8B+ miles of real-world data. Camera-only approach with end-to-end neural nets.
- Mobileye (Intel)
- Powers ADAS in 125M+ vehicles worldwide. SuperVision system driving toward L3 at scale across OEM partners.
- Baidu Apollo
- China's dominant AV platform — 600+ Robotaxi vehicles operating across 11 Chinese cities, largest L4 fleet outside US.
- Cruise (GM)
- Paused operations after 2023 pedestrian incident. Restructuring under GM ownership with new safety protocols.
- Zoox (Amazon)
- Purpose-built bidirectional robotaxi with no steering wheel — designed from scratch for full autonomy, not retrofit.
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Slide 09
Autonomous Trucking
- Freight
- Highway autonomous trucking may be solved before urban self-driving — the environment is more structured, and the economic case is overwhelming.
- Aurora Innovation — Launched commercial driverless truck service in Texas (2024), hauling FedEx and Uber Freight cargo
- Kodiak Robotics — Running L4 trucks on Texas corridors with safety driver present but disengaged
- Gatik — Fixed-route autonomous trucks moving Walmart and Loblaw goods on middle-mile routes
- Driver shortage — US short 80,000 truck drivers now; expected to reach 160,000 by 2030
- Economics — Human driver cost: ~$0.45/mile. AV truck: projected $0.25/mile at scale
- $4Tglobal trucking industry
- 80Ktruck driver shortage in USA today
- 44%of truck driving time spent on highways — easiest AV use case
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Slide 10
Transforming Urban Mobility
- Mobility
- Robotaxi fleets — Waymo One, Cruise, and Baidu running commercial paid rides without human drivers in select cities
- Cheaper rides — Removing the driver (60% of ride cost) could make on-demand rides cheaper than car ownership
- Parking reclaimed — AVs can drive home or to cheaper lots — freeing 30% of urban space currently used for parking
- Curb-to-curb access — Elderly, disabled, and non-driving populations gain full independent mobility for the first time
- Reduced congestion — Platooning, coordinated intersections, and optimized routing cutting urban congestion by 40%+
- Car ownership decline — Morgan Stanley projects personal car ownership dropping 25% in AV-dense cities by 2035
- "The car of the future will be a computer on wheels. The transportation model of the future will be mobility as a service."
- — Mary Barra, GM CEO
- 30%of urban land currently used for parking
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Slide 11
The $13 Trillion Opportunity
- Market
- Autonomous vehicle technology could unlock $13T in annual economic value by 2035 — the largest economic disruption since the internet, spanning mobility, logistics, real estate, and manufacturing.
- $13Tannual economic value by 2035 (Morgan Stanley)
- 90MAV-capable vehicles projected by 2030
- $550BAV market size by 2026
- 40%reduction in total urban congestion from full AV adoption
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Slide 12
Driver Assistance Today
- ADAS
- Before full autonomy, Advanced Driver Assistance Systems (ADAS) are already saving lives in hundreds of millions of vehicles globally.
- AEB
- Automatic Emergency Braking — now mandatory in all US new vehicles, preventing rear-end collisions
- Lane Keep
- Lane Keeping Assist gently steers back to center, preventing road departure crashes
- Adaptive CC
- Adaptive Cruise Control maintains safe following distance automatically in highway traffic
- Blind Spot
- Radar-based blind spot monitoring warning of vehicles in adjacent lanes during lane changes
- NHTSA study: AEB reduces rear-end crashes by 50%, pedestrian crashes by 27%, and injury severity by 35% — already saving thousands of lives annually before full autonomy arrives.
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Slide 13
Regulatory Frameworks
- Policy
- USA — State-level patchwork: California, Texas, Arizona most permissive. NHTSA developing federal AV framework. No unified national law.
- EU — UNECE regulations (UN-R157) allow L3 on highways up to 130 km/h. Germany first to permit L4 nationally (2021).
- China — National standards enabling commercial L4 robotaxis in designated zones; Baidu and WeRide already operating.
- Liability — Key open question: when an AV crashes, who is responsible — the manufacturer, the software company, or no one?
- Insurance — New product-liability insurance models emerging; RAND proposes national data-sharing mandate for AV safety data.
- The Cruise Incident (2023)
- A Cruise robotaxi struck a pedestrian, then drove over her while waiting for guidance. California suspended Cruise's permit. The incident highlighted the gap between capability and safety culture in AV deployment.
- Lesson: Technical capability is necessary but not sufficient — transparency, incident response, and regulatory trust are equally critical.
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Slide 14
HD Maps and Digital Roads
- Infrastructure
- High-definition maps — accurate to 5–10 cm — give AVs context that sensors alone cannot provide: lane topology, speed limits, traffic light positions, and road curvature.
- HERE Technologies — HD Live Map covering major highways in Europe and North America, updated in real time
- TomTom — HD maps for 50+ countries, used by BMW, Volkswagen, and Ford ADAS systems
- Waymo's secret weapon — Proprietary ultra-HD maps of every street in operational cities built by mapping vehicles
- Map limitation — Construction, road changes, and new roads can instantly make maps obsolete
- Crowdsourced update — Tesla uses fleet data to update maps; Mobileye's REM technology does same for OEM partners
- Map-Dependent vs. Mapless
- Map-heavy (Waymo): Rich prior knowledge, very safe in mapped areas, can't operate in unmapped zones.
- Mapless (Tesla): Sensors only, can operate anywhere, but must reason from scratch on every drive.
- Hybrid future: Lightweight lane-level maps + powerful on-board perception — best of both worlds.
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Slide 15
Vehicle-to-Everything (V2X)
- Connectivity
- V2X communication allows vehicles to share data with other vehicles, infrastructure, pedestrians, and networks — creating a collective intelligence far beyond individual sensor range.
- V2V — Vehicle-to-vehicle: broadcast position, speed, and heading to all vehicles within 300m, enabling cooperative collision avoidance
- V2I — Vehicle-to-infrastructure: traffic lights send signal timing directly to vehicles, enabling perfectly timed "green wave" routing
- V2P — Vehicle-to-pedestrian: smartphones warn approaching AVs of pedestrian crossing intent
- V2N — Vehicle-to-network: real-time road hazard broadcasting to all vehicles in area within milliseconds
- 5G backbone — Ultra-low latency (1ms) 5G enabling safety-critical V2X applications at scale
- The Connected Future
- A fully V2X-connected road network could eliminate 80% of crashes that current on-board sensors cannot prevent by sharing information about hazards 500m+ ahead.
- 300mV2V communication range
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Slide 16
Electric + Autonomous
- Convergence
- The electric vehicle and autonomous vehicle revolutions are deeply intertwined — each accelerating the other's adoption and economics.
- Software-defined platform — EVs are computers on wheels, making AV integration cleaner than ICE vehicles with no transmission or fuel system
- Regenerative braking precision — EV motors provide millisecond-accurate speed control impossible with mechanical brakes
- Centralized power — Single HV battery simplifies powering the massive sensor and compute stack (3–5 kW)
- Shared economics — Robotaxi EVs charging autonomously achieve 22+ hours/day utilization vs. 4% for personal cars
- Tesla's advantage — Building both EV platform and AV software stack gives uniquely tight hardware-software integration
- 22 hrsdaily utilization for robotaxi vs. 4% for owned cars
- $0.18/miprojected Waymo cost at full scale (EV)
- 5 kWpower draw of full AV sensor + compute stack
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Slide 17
Driverless Last Mile & Beyond
- Logistics
- Long-Haul Freight
- Aurora, Torc, and Kodiak running commercial driverless trucks between distribution hubs on fixed Texas highway corridors — the first deployed commercial L4 trucks.
- Middle Mile
- Gatik's fixed-route AVs moving store-to-store inventory for Walmart — same route, every day, fully autonomous with no safety driver.
- Last Mile Delivery
- Nuro's purpose-built delivery bots delivering groceries and takeout in Houston and Mountain View — no cabin for a human, purpose-built for goods.
- Sidewalk Robots
- Starship Technologies operating 4M+ autonomous deliveries in 100+ cities — six-wheeled bots navigating footpaths at 6 km/h.
- Air Delivery
- Amazon Prime Air, Wing (Google), and UPS Flight Forward operating drone delivery with FAA Part 135 certification in select markets.
- Port Automation
- Fully automated container terminals using driverless AGVs — Rotterdam, Hamburg, and Singapore processing 24/7 with zero human operators on the quay.
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Slide 18
The Trolley Problem at Scale
- Ethics
- Autonomous vehicles force society to codify ethical decisions that humans make instinctively in split seconds — turning philosophy into code, at scale.
- Unavoidable crash — When a crash is unavoidable, should the AV prioritize passenger or minimize total harm? Who decides?
- Moral Machine — MIT study of 40M decisions across 233 countries found dramatically different cultural ethics for AV crash priorities
- Data privacy — AVs record everything, everywhere — who owns this data, and what can it be used for?
- Algorithmic bias — Sensor systems perform worse in rain, fog, and at detecting dark-skinned pedestrians — safety equity concerns
- Accountability gap — When no human is driving, existing liability law has no clear defendant for crash injuries
- "We're not programming cars to make ethical decisions. We're programming them to avoid situations where ethical decisions would be required."
- — Chris Urmson, Aurora CEO, Former Waymo CTO
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Slide 19
Jobs and the Workforce
- Society
- AV adoption represents the largest occupational disruption since mechanized agriculture — affecting ~5% of the US workforce who drive for a living.
- 3.5M truck drivers in the US — the most common job in 29 states — face phased displacement over 10–20 years
- 4M ride-hail/taxi drivers globally at risk as robotaxi fleets scale and reduce driver income
- New jobs created — AV technicians, remote monitoring operators, fleet managers, AI trainers, safety validators
- Gradual transition — Highway automation first, then urban; phased by geography and route type
- Retraining programs — ATA, AFL-CIO, and state governments developing transition funds and apprenticeship programs
- 3.5MUS truck drivers facing long-term disruption
- 10–20years for full displacement — gradual, not overnight
- 2.5×more AV-related jobs created vs. displaced per McKinsey
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Slide 20
Billions of Simulated Miles
- Testing
- Real-world testing alone cannot cover the long tail of rare scenarios — simulation is how AV companies train and validate their systems at superhuman scale.
- Waymo Simulation — Runs 15 billion simulated miles per year; recreates every real-world crash in virtual replays
- Scenario generation — AI creates novel dangerous scenarios never seen in real driving — generative adversarial testing
- Physics accuracy — Photorealistic simulation with accurate sensor models (LiDAR, camera) for realistic validation
- Hardware-in-the-loop — Real compute stacks tested against simulated environments to validate latency and edge cases
- Open simulation — CARLA, AirSim, and nuPlan enabling academic and startup AV research without expensive real-world testing
- 15Bsimulated miles per year (Waymo)
- 4×faster improvement via simulation vs. real driving
- 100Bmiles needed to statistically prove L4 safety
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Slide 21
Robotaxis Now
- Current State
- Commercial driverless robotaxi services are real and operating today — limited in geography but rapidly expanding.
- Waymo One — Phoenix, San Francisco, Los Angeles, Austin: 100,000+ paid rides per week (2024). App-based, 24/7.
- Baidu Apollo Go — Wuhan, Beijing, Shenzhen: largest robotaxi fleet in China with 600+ vehicles, 1M+ rides given
- WeRide — Operating in Abu Dhabi, Singapore, and Guangzhou — first international robotaxi operations
- Pricing — Waymo One priced comparably to Uber/Lyft today, expected to be significantly cheaper as fleet scales
- Rider experience — 4.9/5 average rating; riders consistently report smoother, more predictable rides than human drivers
- Waymo One Stats (2024)
- 100K+paid rides per week
- 5 citiesoperational in USA
- $5.5Braised in 2024 for expansion
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Slide 22
The US-China AV Race
- Geopolitics
- Autonomous vehicle leadership is a strategic priority for both the US and China — whoever dominates will control the future of mobility, logistics, and urban infrastructure.
- China's advantage — 6,000+ AV test vehicles, $1B+ in government funding, looser data privacy laws enabling more data collection
- Baidu Apollo — Operating in 70+ cities, accumulating more urban driving data than any other single company globally
- Smart city integration — China building entire new districts with V2X infrastructure and AV-first street design from the ground up
- US strengths — Waymo's 15-year head start, access to top global AI talent, and dominant semiconductor supply chain
- Regulatory divergence — China's centralized approach enables faster deployment; US fragmentation creates state-by-state market
- 6,000+AV test vehicles in China
- 70+Chinese cities with commercial AV operations
- $15BChina state investment in AV infrastructure
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Slide 23
The Hard Problems
- Challenges
- Long tail of edge cases — Flooding, construction, unusual signage, emergency vehicles — each rare scenario must be individually solved
- Adverse weather — Heavy rain, snow, and ice degrade sensor performance dramatically — LiDAR returns white noise in blizzards
- Unstructured environments — Unpaved roads, parking lots, and rural driving with no lane markings remain largely unsolved
- Adversarial actors — Vandals covering sensors, cyclists deliberately confusing AVs, and bad actors exploiting AV predictability
- ODD limitation — Today's L4 systems work only within narrow Operational Design Domains — geography, speed, weather conditions
- Cost — Full AV sensor + compute stack adds $15,000–$100,000 to vehicle cost, requiring massive scale to amortize
- "The last few percentages of performance are disproportionately expensive. The first 90% of the problem is achievable; the final 10% is where the real work is."
- — Kyle Vogt, Co-founder Cruise
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Slide 24
Learning from Aviation
- Analogy
- The aviation industry's path to autopilot — now flying 99% of every flight — offers the clearest parallel to where AV technology is heading.
- Autopilot (1914) — Sperry demonstrated basic autopilot; fully trusted only after decades of safety data accumulation
- Today — Commercial aircraft autolands in zero visibility. Pilots manage systems; autopilot executes.
- TCAS analogy — Collision avoidance systems (TCAS) dramatically cut mid-air collisions just as AEB is doing for cars
- Black box requirement — Aviation's mandatory event recorders revolutionized safety analysis; similar mandates likely for AVs
- Certification rigor — FAA's DO-178C software standard for aviation safety — AV industry developing equivalent frameworks
- Aviation Safety Timeline
- 1914
- First autopilot
- Demonstrated but not trusted for years
- 1950s
- Instrument landing
- Autoland in low visibility conditions
- 1970s
- TCAS collision avoidance
- Mandatory after multiple mid-air crashes
- 2024
- Aviation fatality rate
- 0.07 deaths per billion passenger-km — 15× safer than driving
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Slide 25
Insurance and Data Economics
- Business
- Liability shift — As automation increases, liability moves from driver to manufacturer — AV companies will bear insurance costs directly
- Actuarial revolution — AV data enables real-time, behavior-based pricing rather than demographic proxies (age, gender)
- Swiss Re and Munich Re — Major reinsurers developing AV-specific products; partnering with Waymo and others on data sharing
- Black box data — Every AV generates terabytes of data per hour — crash data, near-miss events, route patterns
- Data as revenue — AV fleet data worth billions to city planners, insurers, retailers, and advertisers
- The Data Business Model
- The AV business may ultimately be less about charging per ride and more about monetizing the world's most comprehensive mobility data:
- HD mapping data licensed to cities
- Curb analytics sold to retailers
- Insurance telematics for all vehicles
- Urban planning intelligence to municipalities
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Slide 26
Redesigning Cities
- Cities
- Full AV adoption will fundamentally reshape how cities are designed — reclaiming space from roads and parking lots, and enabling new urban densities.
- Parking reclaimed — 30% of downtown land freed from parking — converted to housing, parks, and commercial space
- Narrower lanes — AVs need 30% less lane width than human-driven cars; streets can be redesigned for people
- 20-minute cities — AV fleets enable true on-demand transit in suburbs, reducing commute times and enabling suburban densification
- Freight tunnels — Elon Musk's Boring Company + AV tunnels moving freight underground, clearing surface streets
- Sleep while commuting — Long commutes become productive time — extending viable commute distances by 50%
- 17%of US land area dedicated to roads and parking
- 800Bhours/year Americans spend commuting
- $400Bvalue of urban parking land in top 20 US cities
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Slide 27
The Long Tail Problem
- Reality
- The toughest AV engineering challenge: rare but real scenarios that demand safe handling even when training data is sparse.
- Emergency Vehicles
- Correctly yielding to police, fire, and ambulance vehicles in complex urban intersections with conflicting signals remains an unsolved edge case.
- Debris & Obstacles
- Mattresses, christmas trees, and cardboard boxes on highways — distinguishing driveable clutter from solid obstacles requires semantic reasoning.
- School Zones
- Children behave unpredictably — darting between parked cars, ignoring crosswalk signals, riding bikes erratically in complex social contexts.
- Road Workers
- Construction zones with temporary signage, flaggers giving non-standard directions, and lane configurations not matching HD maps.
- Animals
- Deer, dogs, coyotes, and livestock behave unpredictably in rural environments where training data is sparse and behavior models fail.
- Social Negotiation
- Four-way stop "who goes first" negotiations rely on implicit human signals — eye contact, hand gestures — that AVs struggle to read.
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Slide 28
AV 2030 Roadmap
- Roadmap
- Near Term (2025–2027)
- Waymo expands to 20+ US cities
- Aurora trucks on all Texas–Southwest corridors
- L3 approved for highways in EU and USA nationally
- Sensor costs fall below $1,000 per vehicle
- Mid Term (2027–2030)
- Robotaxi rides cheaper than human driver equivalent
- AV trucking on all US interstate corridors
- 50+ city robotaxi operations globally
- First mass-market L4 consumer vehicles released
- Horizon (2030+)
- L5 demonstrated in limited environments
- Car ownership declining in major cities
- Urban parking garages converted to housing
- AV-first city districts built in Asia
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Slide 29
Beyond the Car
- Impact
- Safety: 1.35M annual deaths becomes a solvable engineering problem, not an accepted casualty of mobility
- Inclusion: elderly and disabled populations gain full independent mobility for the first time
- Productivity: 800 billion commuter-hours per year in the US alone reclaimed for work, rest, or leisure
- Real estate: urban parking land worth hundreds of billions repurposed for housing and green space
- Energy: optimized AV routing cutting transportation energy use by 30% through smooth driving and platooning
- Insurance: $260B US auto insurance market fundamentally restructured as product liability replaces driver coverage
- "The automobile was the most transformative technology of the 20th century. The autonomous vehicle will be the most transformative of the 21st."
- — Tony Seba, RethinkX
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Slide 30
The Driverless Future Is Arriving
- Summary
- Autonomous vehicles are no longer science fiction — they are carrying paying passengers in cities today. The next decade will see this transform from a limited urban experiment into the dominant mode of transport for goods and people globally.
- 1.35Mlives to save annually
- $13Teconomic value by 2035
- 800Bhours reclaimed from commuting
- L4→L5the final frontier of mobility
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