A 100-hour Autopilot endurance run isn’t a YouTube stunt or a vanity metric. It’s a stress test that exposes what Tesla’s driver-assistance stack actually delivers when the novelty wears off and the miles stack up. For Cybertruck buyers, especially those planning to daily-drive a 6,600-pound stainless-steel wedge, sustained performance matters more than a flawless 10-minute demo.
Reliability Over the Long Haul
Autopilot systems can look brilliant in short bursts, but endurance is where the cracks usually show. Over 100 continuous hours, sensor calibration drift, camera contamination, thermal management of the FSD computer, and software edge cases all come into play. This kind of runtime reveals whether the Cybertruck’s vision-only approach remains consistent as lighting changes, weather cycles through rain and glare, and road conditions vary from smooth interstate to scarred urban asphalt.
What matters here isn’t perfection; it’s stability. A system that degrades gracefully, alerts the driver clearly, and avoids compounding errors earns trust. For buyers, this answers a critical question: will Autopilot feel like a dependable co-driver on day five of a road trip, not just day one?
Driver Supervision Is Still the Real Bottleneck
A 100-hour test makes one thing painfully clear: this is still a Level 2 system, regardless of branding. The longer Autopilot runs, the more often it demands human confirmation, torque input, or intervention when lane markings fade or traffic behavior gets unpredictable. Cybertruck’s steer-by-wire and yoke-style steering add another layer to that interaction, changing how fatigue and attentiveness play out over long stints.
For prospective owners, this endurance test recalibrates expectations. Autopilot reduces workload, but it does not eliminate responsibility. Over extended use, the mental tax of supervision becomes just as important as the system’s technical capability.
Real-World Usability in a Vehicle This Size
The Cybertruck isn’t a Model 3 with a bed slapped on. Its width, flat flanks, and unique sightlines challenge any automated driving system, especially in construction zones, narrow lanes, and dense traffic. Watching Autopilot manage these dimensions hour after hour shows how confidently it places the truck within a lane and how conservatively it reacts to close-proximity vehicles.
This matters to buyers who plan to tow, haul, or commute in crowded metros. Endurance testing highlights whether Autopilot feels like an asset in a full-size truck or a feature tuned primarily for smaller Teslas.
How Close Tesla Really Is to Hands-Off Autonomy
After 100 hours, the gap between marketing language and lived reality becomes impossible to ignore. The system can handle long highway stretches with minimal drama, but it still stumbles in complex merges, aggressive human traffic, and poorly marked roads. Interventions aren’t rare; they’re part of the rhythm.
For Cybertruck buyers, that honesty is valuable. This kind of test doesn’t diminish Autopilot’s achievements—it contextualizes them. It shows exactly how far Tesla has pushed consumer-grade autonomy, and just as importantly, how far there still is to go before hands-off driving becomes more than a promise.
Test Setup: Route Selection, Traffic Mix, Weather Exposure, and Safety Protocols
To understand what those 100 continuous hours really say about Cybertruck Autopilot, the test setup had to remove novelty and force consistency. This wasn’t a highlight reel stitched together from ideal drives. It was a deliberately punishing mix of routes, conditions, and traffic designed to expose patterns—good and bad—over time.
Route Selection: Highways, Surface Streets, and the In-Between
The route plan leaned heavily on repetition rather than spectacle. Long interstate runs formed the backbone, where Autopilot is strongest and lane geometry is predictable. These were interspersed with urban arterials, suburban sprawl, and connector roads that blur the line between highway and city driving.
Construction zones and older road networks were intentionally included. Faded lane markings, mismatched signage, and abrupt merges are exactly where Level 2 systems show their seams. Over dozens of passes, it became clear whether Cybertruck Autopilot was learning the environment or simply surviving it.
Traffic Mix: From Empty Lanes to Human Chaos
Traffic density varied by design, not convenience. Early-morning low-traffic runs tested how smoothly Autopilot managed speed control and lane centering without external pressure. Rush-hour congestion, aggressive cut-ins, and unpredictable human behavior filled the other end of the spectrum.
This mattered more in a Cybertruck than a smaller Tesla. Its width and slab-sided body exaggerate close calls, and Autopilot’s conservative following distances often triggered reactions from impatient drivers. Watching how the system handled social driving dynamics over 100 hours revealed as much as its raw technical competence.
Weather Exposure: Letting the Sensors Struggle
The test didn’t wait for perfect conditions. Rain, glare-heavy sunsets, overcast afternoons, and night driving were all part of the cycle. While extreme weather wasn’t chased, the goal was exposure, not avoidance.
Camera-based perception showed predictable weaknesses. Heavy spray, reflective pavement, and low-contrast lane markings increased alerts and disengagements. Over time, these moments stopped feeling random and started forming a pattern—valuable insight into how environmental stress compounds driver workload.
Safety Protocols: Human in the Loop, Always
Despite the endurance framing, this was never a hands-off experiment. A fully attentive driver remained in the seat at all times, hands hovering near the yoke, eyes scanning mirrors and traffic. Torque input and system prompts were logged, along with every disengagement, alert, and manual correction.
Redundancy was built into the process. Breaks were scheduled to manage fatigue, cameras monitored driver attention, and no attempt was made to override Autopilot safeguards. That discipline matters, because pushing a Level 2 system beyond its design limits doesn’t reveal autonomy—it reveals risk.
Why This Setup Matters for Interpreting the Results
A 100-hour test only means something if the environment keeps pushing back. By standardizing routes while varying traffic and weather, the test exposed how Autopilot behaves when familiarity increases but conditions don’t cooperate. That’s where reliability is truly measured.
For prospective Cybertruck owners, this setup mirrors real ownership. Commutes repeat, roads degrade, weather shifts, and traffic never behaves the same way twice. The structure of this test is what allows the results to speak honestly about supervision demands and how far Tesla still is from true hands-off autonomy.
Inside the Cybertruck’s Autopilot Stack: Hardware, Software Version, and Sensor Limitations
Understanding what those 100 hours actually prove requires peeling back the Cybertruck’s autonomy stack. Autopilot behavior isn’t just about miles logged; it’s a product of compute power, sensor fidelity, and the specific software logic making decisions in real time. The endurance test effectively stress-tested every layer at once.
Hardware 4: Tesla’s Most Capable Compute Yet
The Cybertruck runs Tesla’s Hardware 4 platform, a meaningful leap over the HW3 systems still on many Model 3s and Ys. Processing bandwidth is higher, neural network throughput is improved, and camera resolution is significantly increased, all aimed at better long-range perception and faster reaction times.
Over 100 continuous hours, that extra compute headroom mattered. The system showed fewer “hesitation stalls” when interpreting dense traffic or complex merges, especially on familiar routes. It didn’t eliminate mistakes, but it reduced the frequency of indecision that often forces driver intervention on older hardware.
Vision-Only Sensors: Cameras Doing All the Heavy Lifting
Like all current Teslas, the Cybertruck relies exclusively on cameras for external perception. There’s no radar, no lidar, and no ultrasonic sensors backing up low-speed or proximity judgments. Eight exterior cameras feed the neural networks, with placement optimized for forward and lateral coverage rather than redundancy.
That design choice shaped the entire test. In clear daylight, lane tracking and vehicle identification were confident and repeatable. But rain spray, glare off wet pavement, and faded lane markings consistently taxed the system, confirming that vision-only autonomy still struggles when contrast and visibility degrade.
Software Context: FSD Supervised, Not Autonomy
The truck was running a modern FSD Supervised build from the v12 generation, which unifies city streets and highway logic under a single neural network stack. This matters, because behavior is now learned rather than rule-based, producing smoother steering and more humanlike throttle modulation.
Across 100 hours, the software demonstrated consistency rather than progression. It didn’t “learn” the route in a meaningful way, but it did behave predictably once its tendencies were understood. That predictability is useful, yet it reinforces the reality that the system still depends on active driver oversight.
Driver Monitoring and the Reality of Supervision
Inside the cabin, the driver-monitoring camera played a constant role. Attention checks were frequent, especially during long, low-complexity stretches where vigilance naturally fades. The system tolerated brief glances away, but sustained inattention triggered escalating alerts and eventual disengagement.
This is where the 100-hour duration becomes revealing. Mental fatigue accumulated faster than system confidence, highlighting that Autopilot reliability isn’t the limiting factor—human supervision is. That gap is a critical indicator of how far Tesla remains from true hands-off autonomy.
Physical Design Quirks That Influence Perception
The Cybertruck’s stainless-steel body and flat windshield introduce unique variables. Glare, reflections, and water behavior across the glass affected camera clarity more than expected during certain lighting conditions. Wiper coverage helped, but it didn’t fully mitigate edge distortion in heavy rain.
These aren’t deal-breakers, but they are reminders that autonomy lives at the intersection of software and physical design. Over long durations, small optical compromises compound into moments where the driver must step in—not because the truck is incapable, but because its sensors are operating at the edge of their comfort zone.
Hour-by-Hour Performance Trends: What Changed as the Miles and Fatigue Added Up
The most telling part of a 100-hour run isn’t a single dramatic failure. It’s the slow, cumulative shift in how the system behaves as conditions, lighting, traffic density, and driver alertness cycle again and again. Over time, patterns emerged that short demo drives simply can’t surface.
Hours 1–10: Baseline Confidence and Clean Inputs
In the opening hours, the Cybertruck’s behavior was textbook FSD v12. Lane centering was calm, steering inputs were fluid, and longitudinal control felt appropriately assertive without being abrupt. With fresh sensors, a focused driver, and predictable traffic, the system inspired confidence quickly.
This is the version of Autopilot most owners recognize. Smooth merges, competent lane changes, and minimal nags when the driver’s eyes stayed forward. If the test ended here, you’d walk away thinking the system is nearly finished.
Hours 10–30: Repetition Exposes Personality
As the miles piled on, Autopilot’s tendencies became more apparent. The Cybertruck showed a consistent preference for conservative following distances and early braking, especially in mixed-speed traffic. It wasn’t wrong, but it was cautious in a way that sometimes disrupted traffic flow.
Lane selection also revealed a bias toward staying put rather than aggressively positioning for upcoming exits. Over hours, that predictability became easier to manage as a driver, but it underscored that this is still a system optimized for safety margins, not assertive human-style driving.
Hours 30–55: Night Driving and Cognitive Load
Extended nighttime operation changed the dynamic. The truck handled lane markings and headlights well, but construction zones and reflective signage triggered more micro-corrections. Steering remained smooth, yet confidence dipped slightly in poorly marked areas.
This is where driver fatigue started to matter more than software capability. Attention checks felt more intrusive simply because staying mentally engaged for that long is hard. The system didn’t degrade, but the human-machine partnership did.
Hours 55–75: Weather, Glare, and Sensor Edge Cases
Rain and changing light angles highlighted the Cybertruck’s physical realities. Water sheeting across the flat windshield and low sun glare introduced brief moments of hesitation. The system slowed preemptively, occasionally to the point of being overly cautious.
Interventions increased here, not due to outright errors, but because the driver could see uncertainty building before the system crossed a threshold. This is a critical distinction: Autopilot often knew its limits, but required human judgment to bridge the gap smoothly.
Hours 75–100: Stability Without Adaptation
By the final stretch, one thing was clear: the system doesn’t evolve over time. Performance stabilized rather than improved, delivering the same decisions it made on hour ten, just under different conditions. There was no learning curve, only repetition.
What did change was tolerance. Small quirks that felt minor early on became more noticeable as fatigue set in. That doesn’t indict the software so much as it reframes the autonomy question. Over 100 hours, Autopilot proved reliable, but it also proved that true hands-off driving isn’t just about software competence—it’s about reducing the mental tax on the human in the seat.
Critical Interventions and Edge Cases: When and Why the Driver Had to Take Over
After 100 hours, the pattern of disengagements told a clearer story than any single scare moment. These weren’t dramatic “system failure” events, but calculated human overrides when Autopilot hesitated, misread intent, or optimized too hard for caution. The takeovers revealed where Tesla’s vision-based stack is strong—and where it still leans heavily on a vigilant driver.
Construction Zones and Temporary Geometry
Temporary lane shifts were the single biggest trigger for interventions. Cones, jersey barriers, and repainted lines confused the system’s lane model, especially when the old markings were still faintly visible. The Cybertruck would often slow abruptly or drift toward the safest perceived corridor, forcing the driver to assert a more decisive path.
This isn’t a sensor failure so much as a logic one. Humans infer intent from context—workers, equipment, traffic flow—while Autopilot remains bound to what it can visually confirm. In long construction stretches, that gap became tiring to manage.
Merging Traffic and Human Negotiation
On-ramps and zipper merges exposed Autopilot’s lack of social driving instincts. The truck could hold a lane perfectly, but it struggled to negotiate space with aggressive or indecisive human drivers. When other vehicles pushed the envelope, Autopilot defaulted to braking rather than asserting position.
Drivers stepped in not because the maneuver was unsafe, but because it disrupted traffic flow. Over 100 hours, this repeated enough to underscore a key limitation: Autopilot can follow rules, but it can’t yet read the room.
Low-Speed Urban Chaos
Dense urban areas introduced a different class of edge case. Delivery vans stopping unpredictably, pedestrians hovering near curbs, and cyclists threading gaps all demanded rapid prioritization. Autopilot handled these scenarios conservatively, sometimes freezing in a way that signaled uncertainty.
The driver takeover here was about momentum and clarity. Humans can make quick, confident micro-decisions that keep traffic moving without compromising safety. Autopilot, by contrast, waits for certainty—and in cities, certainty rarely comes.
Weather-Induced Sensor Ambiguity
As noted earlier, rain and glare didn’t break the system, but they narrowed its confidence envelope. Heavy spray from semis and smeared reflections off the Cybertruck’s steep windshield geometry occasionally caused brief lane uncertainty. The truck would decelerate smoothly, but sometimes too much for surrounding traffic.
These were moments where a human could see through the ambiguity faster than the cameras could resolve it. Intervening early prevented the system from cascading caution into disruption, a subtle but important distinction over marathon drives.
Attention Monitoring as a Forcing Function
Finally, some takeovers weren’t about driving at all—they were about compliance. Tesla’s attention monitoring demanded periodic input even when the system was performing flawlessly. Over long stretches, especially at night, this became a friction point.
Ironically, the better Autopilot performed, the more these prompts broke concentration. It reinforced a central truth from the 100-hour test: Tesla Autopilot is a highly capable driver assist, but it still requires an actively engaged human to manage its blind spots, interpret intent, and shoulder responsibility when the world gets messy.
Highway Confidence vs. Urban Reality: Where Cybertruck Autopilot Excelled and Struggled
The contrast became impossible to ignore once the test miles stacked up. After wrestling with city ambiguity and sensor uncertainty, pointing the Cybertruck at open interstate felt like switching operating systems. This is where Tesla’s Autopilot logic finally had room to breathe—and it showed.
Interstate Miles: Autopilot in Its Element
On divided highways with clear lane markings, consistent traffic flow, and limited cross-traffic, Autopilot settled into a confident rhythm. Lane centering was rock-solid, with none of the nervous micro-corrections that plagued tighter environments. The Cybertruck tracked smoothly at speed, its wide stance and stiff chassis giving the cameras a stable platform to work from.
Adaptive cruise control was particularly well-calibrated over long stints. Following distances were conservative but predictable, and speed adjustments felt human rather than algorithmic. Over dozens of uninterrupted highway hours, the system demonstrated something critical: repeatability. It behaved the same at hour five as it did at hour ninety-five, a quiet indicator of underlying system reliability.
On-Ramps, Off-Ramps, and the Limits of Context
Even on highways, the cracks appeared in transitional zones. Merging traffic exposed Autopilot’s tendency to wait rather than negotiate, often yielding too much space when assertiveness would have improved traffic flow. The system understands lanes exceptionally well, but it still struggles to infer intent from surrounding vehicles.
Exiting highways highlighted a similar gap. The Cybertruck could follow navigation prompts accurately, but late lane changes or ambiguous ramp markings occasionally triggered hesitation. These moments weren’t dangerous, but they required supervision to avoid becoming a rolling obstacle for faster-moving human drivers.
Urban Streets: Too Many Variables, Not Enough Judgment
Dropping back into city environments reversed the confidence equation entirely. Stop-and-go traffic, unprotected turns, and human unpredictability overwhelmed the system’s rule-based caution. Autopilot could identify objects, lanes, and signals, but it often failed to prioritize them the way a human instinctively does.
The Cybertruck’s size amplified this effect. Wide shoulders and sharp steering inputs in tight corridors made conservative decisions feel even more intrusive. Over 100 hours, urban driving consistently demanded the most interventions, reinforcing that Autopilot is still optimized for structured flow, not social negotiation.
What 100 Hours Really Revealed
The long-duration test clarified Tesla’s autonomy gap more effectively than any short demo ever could. On highways, Autopilot is already a fatigue-killing co-pilot that reduces cognitive load and delivers genuine usability. In cities, it remains a capable assistant that still leans heavily on human judgment to resolve edge cases and keep traffic moving.
True hands-off autonomy isn’t blocked by raw vehicle control or sensor coverage here—it’s blocked by interpretation. Until Autopilot can consistently understand intent, urgency, and unwritten rules, the driver remains the final authority. After 100 continuous hours, that reality wasn’t disappointing; it was simply undeniable.
Driver Workload After 100 Hours: Mental Fatigue, Trust Calibration, and Supervision Demands
After seeing where Autopilot excels and where it stumbles, the next question becomes unavoidable: what does 100 continuous hours do to the human behind the wheel? Not the car, not the software roadmap—the driver. Because long-duration autonomy testing isn’t just a stress test for silicon, it’s a psychological endurance run for supervision.
Mental Fatigue: Reduced Effort, Not Reduced Responsibility
On highways, Autopilot dramatically lowers physical workload. Steering micro-corrections, lane centering, and speed modulation are handled with a consistency that genuinely preserves energy over long stints. After eight or ten hours, you step out of the Cybertruck less sore and less drained than you would after manual driving.
But cognitive fatigue doesn’t disappear—it shifts. Instead of actively driving, your brain stays in a semi-alert monitoring mode, scanning mirrors, watching for phantom braking triggers, and anticipating indecision near merges. Over 100 hours, that passive vigilance becomes its own form of exhaustion, one that’s quieter but harder to manage.
Trust Calibration: Learning When Not to Believe the System
Extended exposure forces a recalibration of trust that short tests never reveal. Early on, Autopilot’s competence on highways encourages reliance, even complacency. Then a few poorly timed hesitations—an awkward lane split, a delayed exit decision—snap that trust back into check.
By hour 50 or 60, a pattern emerges. You trust the system implicitly in well-marked lanes at steady speeds, and you preemptively disengage in environments where it has previously hesitated. This isn’t blind faith or rejection—it’s learned behavior, built from repetition, and it underscores how far the system still is from being self-supervising.
Supervision Demands: The Cost of Being the Fallback
Being the legal and practical fallback driver carries a constant tax. Hands hover, eyes dart, and your right foot stays mentally connected to the brake even when Autopilot is in full control. Tesla’s torque-based steering wheel monitoring reinforces that vigilance, preventing total disengagement but also ensuring you never fully relax.
Over 100 hours, this requirement becomes more noticeable than any single driving flaw. The Cybertruck can handle long stretches without issue, but it can’t tell you when it’s about to need help. That means the driver must always assume the next corner case is seconds away, especially in mixed traffic.
Usability vs Autonomy: Where the Line Actually Sits
What these hours ultimately reveal is a sharp distinction between usability and autonomy. Autopilot is an excellent workload reducer in structured environments, and its reliability there is no longer the question. The real limitation is that the system still depends on a human to manage context shifts, intent interpretation, and social driving cues.
That dependency defines the driver’s workload. You’re not driving every second, but you’re responsible every second. Until the system can shoulder that responsibility without requiring continuous human arbitration, the mental load remains firmly in the driver’s seat—even after 100 straight hours of letting the truck do the driving.
Reliability Verdict: What This Test Reveals About Tesla’s Progress Toward Hands-Off Autonomy
Taken as a whole, those 100 uninterrupted hours clarify something the previous sections only hinted at. Autopilot isn’t fragile or erratic anymore—it’s consistent, repeatable, and largely predictable. But consistency alone doesn’t equal independence, and this test draws that line with uncomfortable clarity.
System Reliability: Consistency Without Comprehension
From a pure reliability standpoint, Autopilot deserves credit. It didn’t degrade over time, didn’t exhibit sensor drift, and didn’t accumulate errors the way early driver-assist systems often did. Lane centering, adaptive cruise control, and basic traffic flow management remained stable whether it was hour five or hour ninety-five.
What it still lacks is situational comprehension. The system executes its programmed behaviors faithfully, but it doesn’t truly understand why a situation is changing. When road geometry, traffic intent, or human unpredictability falls outside its learned patterns, Autopilot doesn’t fail explosively—it hesitates, slows, or asks for help.
Driver Supervision: Reliable Enough to Trust, Not Enough to Abandon
This is where the 100-hour duration matters. Over shorter tests, the need for supervision feels reasonable, even minimal. Over days, it becomes the defining limitation of the system.
Autopilot rarely scares you, but it frequently keeps you alert. That’s a subtle but critical distinction. The system is reliable enough that you trust it with vehicle control, yet unreliable enough in edge interpretation that you never stop monitoring the environment yourself.
Real-World Usability: A Highway Champion With Urban Anxiety
In structured highway scenarios, Autopilot operates like a seasoned long-haul co-driver. It manages speed smoothly, tracks lanes confidently, and reduces fatigue in a way no conventional cruise control can touch. For long-distance travel, it’s already a meaningful quality-of-life upgrade.
The moment complexity spikes—construction zones, unclear merges, assertive human drivers—the system’s confidence collapses inward. It doesn’t aggressively make mistakes; instead, it defers. That deference shifts the burden right back to the driver, reinforcing that this is still an assistive system, not a self-sufficient one.
Progress Toward Hands-Off Autonomy: Measurable, But Not Transformational
After 100 hours, Tesla’s progress toward hands-off autonomy feels incremental rather than revolutionary. The Cybertruck’s Autopilot has matured into a dependable tool, but it hasn’t crossed the philosophical threshold where the car assumes responsibility for the drive.
True hands-off autonomy requires not just control accuracy, but contextual authority—the ability to decide when it can handle a situation and when it cannot. This test shows Autopilot still externalizes that judgment to the human. Until the system can internalize that responsibility, the steering wheel remains a requirement, not a backup.
Final Takeaway for Prospective Owners: Should You Trust Cybertruck Autopilot for Long-Haul Use
So what does 100 continuous hours actually tell us? It reveals a system that’s stable, predictable, and genuinely useful—but still fundamentally incomplete. Cybertruck Autopilot proves it can shoulder a massive share of the workload, yet it never fully releases you from responsibility.
Reliability: Consistent Behavior Beats Flashy Promises
Across extended use, Autopilot’s biggest strength is consistency. It doesn’t degrade over time, it doesn’t grow erratic with fatigue, and it doesn’t surprise you with sudden personality shifts. Steering control, adaptive cruise behavior, and lane discipline remain remarkably uniform hour after hour.
That consistency builds trust, but it’s the trust you place in a well-calibrated machine tool, not an autonomous chauffeur. The system behaves exactly as designed—conservatively, cautiously, and with clear limits.
Driver Supervision: Mandatory, Non-Negotiable, and Mentally Taxing
If you’re hoping 100 hours unlocks a point where vigilance fades into the background, it doesn’t. The Cybertruck still requires active mental engagement, even when physical inputs are minimal. Your eyes stay up, your judgment stays engaged, and your hands are never truly off-duty.
This is the tradeoff of Tesla’s current philosophy. Autopilot removes fatigue from throttle and steering, but not from decision-making. On long hauls, that distinction matters as much as seat comfort or range.
Long-Haul Usability: Outstanding Where Roads Behave
On open interstates and well-marked highways, Cybertruck Autopilot is legitimately excellent. It smooths traffic flow, maintains composure at speed, and turns long-distance travel into a calmer, more efficient experience. For cross-state drives, it’s one of the best driver-assist systems you can buy today.
But once the road environment gets ambiguous, the system hands the baton back immediately. That’s not failure—it’s honesty. Autopilot knows when it’s out of its depth, and it refuses to bluff.
Autonomy Reality Check: Powerful Assistance, Not a Paradigm Shift
After 100 hours, one conclusion becomes unavoidable: this is not hands-off autonomy in disguise. Tesla has improved execution, not redefined responsibility. The Cybertruck still depends on you to interpret intent, anticipate human behavior, and arbitrate edge cases.
Progress is real, measurable, and meaningful—but it’s evolutionary. The leap to true autonomy will require systems that don’t just control the vehicle, but own the drive. Cybertruck Autopilot isn’t there yet.
The Bottom Line for Buyers
If you’re a prospective Cybertruck owner who spends serious time on highways, Autopilot is absolutely worth trusting as a long-haul partner. It reduces fatigue, increases consistency, and makes big miles easier to manage. Just don’t mistake trust for surrender.
Think of it as the best co-driver you’ve ever had—one that never gets tired, never gets emotional, and never stops asking you to stay engaged. For now, that balance defines Tesla’s autonomy story, and the Cybertruck executes it better than most.
