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--- en:safeav:maps:instability [2025/07/02 16:00] – pczekalski
+++ en:safeav:maps:instability [2026/03/30 10:33] (current) – airi
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 ====== Sources of Instability ======
 {{:en:iot-open:czapka_m.png?50| Masters (2nd level) classification icon }}
+Instability of perception, mapping, and localization is caused mainly by the uncertainty of the sensor readings. The object recognition could provide different results over time, even though the scene is static, due to sensor noise. The map could be distorted by erroneous readings of GNSS caused by the reflection of signals. Localization could be degraded by occlusions or unexpected objects.
-<todo @bertlluk #bertlluk:2025-06-25></todo>
+There are several sources of uncertainty: sensor noise, error readings, model uncertainty, environment randomness, occlusions, adversarial attacks, and intention estimation errors for other traffic participants. This section categorizes and describes them and provides an introduction to how these phenomena can be handled.
+There are two main types of uncertainty: aleatoric and epistemic.
+Aleatoric uncertainty represents the stochastic nature or randomness of the world. Sensor readings are always affected by different types of noise, and many processes in the environment are of a stochastic nature.
+Epistemic uncertainty, or systematic uncertainty, arises from imprecise or incomplete models.  The model cannot explain the observed data completely or precisely.
+Sensor noise is a significant source of instability in the AV systems. All the sensors providing digital signals add quantization noise inevitably to the measurement, as the nature of the real environment is continuous. To minimize the effects of quantization, a higher resolution is used, causing, on the other hand, a significant increase in computational complexity.
+But quantization is not only a source of noise in the sensors. Different physical processes caused noise, e.g., interference, statistical quantum fluctuations, etc. These types of noise are random and usually have a normal distribution. Therefore, the noise can be reduced by averaging over multiple measurements and other similar methods, but it could introduce a time delay and distortion of data.
+Cheaper sensors are usually more prone to noise. But even the best sensors are not noiseless. According to ISO 26262, HARA should be performed, safety goals should be set, and finally, hardware safety requirements should be specified. The hardware specification may include the noise ratio, etc., of the sensor.
+Sensor fusion is often used to cope with sensor noise.  Different sensors use different physical processes for measurement, and therefore, the noise is different as well. A combination of different sensor modalities can improve the measurement.
+Another source of uncertainty is the limitation of sensing, such as occlusion or limited visibility. The limitation of the perception subsystem limits the performance of the systems relying on the perception, as the information is only partial or not provided at all. There are various approaches to deal with partial occlusion, like object tracking and prediction, the Kalman filter, or sensor fusion. Especially when we take the weather into account, different sensor modalities could work better for different weather conditions. A combination of radar, visible light camera, and infrared cameras of different wavelengths could effectively diminish the effects of the harsh weather conditions ( e.g., fog).
+A similar type of uncertainty is when we have only partial information about the intentions of other agents in the traffic. The prediction of the future vehicle’s trajectory is always a combination of the physical limits of the vehicle and the intention of its driver. The physical limits could be known to a high degree of certainty; the intention of the driver is always only estimated from the observations of previous actions.
+Another sources of uncertainty are traffic regulations themselves. Because the real-world road environment is too intricate to quantify all the regulations one by one, there are ambiguities in traffic regulations that do not provide quantitative criteria.
+A specific source of instability, especially in the context of neural networks, is an adversarial attack. An adversarial attack is an intentional modification of the environment, causing networks to misrecognize an object as a different class or not detect it at all. Even a little pattern added to the environment (e.g. traffic sign) could cause misrecognition.
+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.
+===== Design Challenges =====
+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.
+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.
+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.
+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.
+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.
+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.
-There are several sources of uncertainty: sensor noise, error readings, model uncertainty, environment randomness, occlusions, adversarial attacks, and   intention estimation errors for other traffic participants. This section categorizes and describes them and provides an introduction to how these phenomena can be handled.

en/safeav/maps/instability.1751461249.txt.gz · Last modified: 2025/07/02 16:00 by pczekalski