Analysing Adversarial Threats to Rule-Based Local-Planning Algorithms for Autonomous Driving

The safety and security of navigation and planning algorithms are essential for the adoption of autonomous driving in real-world operational environments. Adversarial threats to local-planning algorithms are a developing field. Attacks have primarily been targeted at trajectory prediction algorithms which are used by the autonomous vehicle to predict the motion of ego vehicles and other environmental objects to calculate a safe planning route. This work extends the attack surface to focus
on a rule-based local-planning algorithm, specifically focusing on  the planning cost-based function, which is used to estimate the safest and most efficient route. Targeting this algorithm, which is used in a real-world, operational autonomous vehicle program, we devise two attacks; 1) deviation to the lateral and longitudinal pose values, and 2) time-delay of the sensed-data input messages to the local-planning nodes. Using a low-fidelity simulation testing environment, we conduct a sensitivity analysis using multiple deviation range values and time-delay duration. We find that the impact of adversarial attack cases is visible in the rate of failure to complete the mission and in the occurrence of safety violations. The cost-function is sensitive to deviations in lateral and longitudinal pose and higher duration of message delay. The result of the sensitivity analysis suggests minor deviations of the pose (lateral, longitudinal) values as an optimal range for the attackers search space. Options for mitigating such attacks are that the AV should run a concurrent process executing a concurrent planning instance for redundancy.