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ARXIV:2605.03491 · AUTONOMOUS DRIVING · SUBMITTED 06 MAY · 20:27 UTC · FRESHNESS STALE
ARXIV:2605.03491AUTONOMOUS DRIVINGSUBMITTED 06 MAY · 20:27 UTCFRESHNESS STALEAdithya Mohan · Xujun Xie · Venkatesh Thirugnana Sambandham · Torsten Schön · arXiv
A framework for evaluating the adversarial robustness of autonomous driving systems using real-world data and multiple trajectory learning methods.
Opportunity summary
Pain A framework for evaluating the adversarial robustness of autonomous driving systems using real-world data and multiple trajectory learning methods.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework for evaluating the adversarial robustness of autonomous driving systems using real-world data and multiple trajectory learning methods. However, purely virtual testing fails to capture structural inconsistencies, supervision constraints, and state-representation effects that…
Most evaluations of autonomous driving policies under adversarial conditions are conducted in simulation, due to cost efficiency and the absence of physical risk. However, purely virtual testing fails to capture structural inconsistencies, supervision constraints,…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results show that state-structure design and architectural inductive biases critically influence adversarial stability, leading to markedly different robustness profiles despite comparable nominal prediction accuracy…
Autonomous Driving moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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A framework for evaluating the adversarial robustness of autonomous driving systems using real-world data and multiple trajectory learning methods.
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10.48550/arXiv.2605.03491A framework for evaluating the adversarial robustness of autonomous driving systems using real-world data and multiple trajectory learning methods.
Abstract
Most evaluations of autonomous driving policies under adversarial conditions are conducted in simulation, due to cost efficiency and the absence of physical risk. However, purely virtual testing fails to capture structural inconsistencies, supervision constraints, and state-representation effects that arise in real-world data and fundamentally shape policy robustness. This work presents an offline trajectory-learning and adversarial robustness evaluation framework grounded in real-world intersection driving data. Within a controlled data contract, we train and compare three trajectory-learning paradigms: Multi-Layer Perceptron (MLP)-based Behavior Cloning (BC), Transformer-based object-tokenized BC, and inverse reinforcement learning (IRL) formulated within a Generative Adversarial Imitation Learning (GAIL) framework. Models are evaluated using Average Displacement Error (ADE) and Final Displacement Error (FDE). Inference-time robustness is assessed by subjecting trained policies to gradient-based adversarial perturbations across multiple intersection scenarios, yielding a structured robustness evaluation matrix. Results show that state-structure design and architectural inductive biases critically influence adversarial stability, leading to markedly different robustness profiles despite comparable nominal prediction accuracy (ADE < 0.08). Inference-time Projected Gradient Descent (PGD) attacks induce final displacement errors of up to approximately 8 meters. The proposed framework establishes a scalable benchmark for studying offline trajectory learning and adversarial robustness in real-world autonomous driving settings.
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PROBLEM
A framework for evaluating the adversarial robustness of autonomous driving systems using real-world data and multiple trajectory learning methods. However, purely virtual testing fails to capture structural inconsistencies, supervision constraints, and state-representation effe...
METHOD
Most evaluations of autonomous driving policies under adversarial conditions are conducted in simulation, due to cost efficiency and the absence of physical risk. However, purely virtual testing fails to capture structural inconsistencies, supervision constraints, and state-repr...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results show that state-structure design and architectural inductive biases critically influence adversarial stability, leading to markedly different robustness profiles despite comparable nominal predict...
WHY NOW
Autonomous Driving moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for evaluating the adversarial robustness of autonomous driving systems using real-world data and multiple trajectory learning methods. However, purely virtual testing fails to capture structural inconsistencies, supervision constraints, and state-representation effects that arise in real-world data and fundamentally shape policy robustness.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Most evaluations of autonomous driving policies under adversarial conditions are conducted in simulation, due to cost efficiency and the absence of physical risk. However, purely virtual testing fails to capture structural inconsistencies, supervision constraints, and state-representation effects that arise in real-world data and fundamentally shape policy robustness.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results show that state-structure design and architectural inductive biases critically influence adversarial stability, leading to markedly different robustness profiles despite comparable nominal prediction accuracy (ADE < 0.08). Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Autonomous Driving moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for evaluating the adversarial robustness of autonomous driving systems using real-world data and multiple trajectory learning methods.
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Autonomous Driving
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