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| en:safeav:maps:instability [2025/10/17 17:04] – kosnark | en:safeav:maps:instability [2026/03/30 10:33] (current) – airi |
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| Adversarial attacks are especially threatening for autonomous driving systems, which may harm human life. The robustness of autonomous driving systems against adversarial attacks is called SOTIF (Safety Of The Intended Functionality) and is covered by international standards such as ISO 21448. | Adversarial attacks are especially threatening for autonomous driving systems, which may harm human life. The robustness of autonomous driving systems against adversarial attacks is called SOTIF (Safety Of The Intended Functionality) and is covered by international standards such as ISO 21448. |
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| | ===== Design Challenges ===== |
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| | Designing autonomous systems which perform reliability has many design challenges. For the front-end of the AV pipeline discussed in this chapter, the challenges center around gracefully working across a range of operating conditions (ODD), performance characteristics of the sensors, and supply chain concerns. |
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| | Weather is a fundamental source of uncertainty for autonomous systems because it directly degrades sensor performance, but its impact varies significantly across ground, airborne, marine, and space domains. On the ground, rain, fog, snow, and dust can severely impair optical sensors (cameras, lidar) through scattering, attenuation, and occlusion, while also affecting radar through multipath and clutter—making perception and object classification the primary bottlenecks for autonomous vehicles. In airborne systems, weather effects such as icing, turbulence, and convective storms influence both sensing and vehicle dynamics; however, aviation benefits from structured sensing (e.g., radar, inertial systems, GPS) and well-developed weather-avoidance procedures, allowing autopilot systems to remain robust as long as hazardous regions are avoided. Marine systems face persistent challenges from sea spray, wave motion, and low-contrast environments, which degrade vision systems and introduce instability in sensor measurements, though radar and sonar provide complementary resilience. In space, traditional “weather” is absent, but analogous environmental effects—such as solar radiation, cosmic rays, and thermal extremes—impact sensor reliability and electronics, requiring radiation-hardened designs and redundancy. Across all domains, the key distinction is that weather (or its equivalent) not only reduces sensor fidelity but also increases uncertainty in state estimation and decision-making, making sensor fusion, redundancy, and probabilistic reasoning essential for maintaining safe autonomous operation. |
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| | Further, the use of electromagnetic (EM) energy in modern transportation corridors is increasing rapidly, driven by three major factors. First, the expansion of cellular networks to support continuous telecommunications for travelers has intensified ambient EM activity. Second, the widespread integration of active sensors—such as radar and LiDAR—within vehicles has introduced additional high-frequency sources. Third, infrastructure operators are deploying active sensing technologies in Roadside Units (RSUs) to enable vehicle-to-infrastructure (V2I) communication and monitoring. The resulting concentration of active EM sources is relatively well understood in the visual band with care taken for the design of highly reflective civil infrastructure as well as methods for night-time interference. However, this same care has not been done for all the sensor modalities. Especially for ground and airborne (air taxi corridors), active sensors create dense EM energy corridors which raise new challenges related to interference, coexistence, and safety which have not been characterized. |
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| | Beyond weather and EMI, sensor modalities must be complete enough to provide coverage under the constraints of the civil engineering infrastructure. Important aspects include the handling of curves, on/off ramps, bridges, tunnels, and more. For a designer there is a complex tradeoff between sensor type, number of sensors, and cost of sensors. For airborne, marine, and space systems, power and weight are also primary concerns. |
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| | Finally, because of the semiconductor business structure, cost and supply chain are intimately connected. The relationship between cost and volume in semiconductors is fundamentally shaped by high fixed costs and low marginal costs, creating powerful economies of scale. Semiconductor manufacturing requires enormous upfront investment in fabrication facilities (fabs), process development, and mask sets—often totaling billions of dollars—while the incremental cost of producing each additional chip (once the fab is running) is relatively low. As production volume increases, these fixed costs are amortized over a larger number of units, driving down the cost per chip. This dynamic is reinforced by learning curve effects (often described by Wright’s Law), where yield improvements, process optimizations, and defect reduction further reduce per-unit costs with cumulative volume. However, this relationship is not linear: advanced nodes (e.g., sub-5nm) introduce escalating mask and tooling costs that require extremely high volumes to be economically viable, while lower-volume or specialized chips (e.g., automotive, aerospace) often rely on mature nodes where costs are more stable but less aggressively optimized. As a result, the semiconductor industry exhibits a strong coupling between scale, technology node, and market demand, with leading-edge innovation economically justified primarily in high-volume applications such as consumer electronics and data center computing. |
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| | Advanced semiconductors can offer significant performance improvements in function, power, and cost. However, the economics of volume often determine whether the chip will be built. Today, the semiconductor cycle is dominated by consumer products. Automotive markets offer mid-tier volumes, and the other modalities (airborne, space, marine) are very low volume markets. The resulting design challenge is to either use advanced semiconductor chips from the consumer market, but with the limitations on safety. Alternatively, use lower-tier semiconductor chips but live with performance/power/cost/weight challenges. |
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