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Accurately determining responsibility in traffic accidents is crucial for ensuring fairness in law enforcement and optimizing responsibility standards. Traditional methods predominantly rely on subjective judgments, such as eyewitness testimonies and police investigations, which can introduce biases and lack objectivity. To address these limitations, we propose the AMFormer(Arithmetic Feature Interaction Transformer) framework—a deep learning model designed for robust and interpretable traffic accident responsibility prediction. By capturing complex interactions among key factors through spatiotemporal feature modeling, this framework facilitates precise multi-label classification of accident responsibility. Furthermore, we employ SHAP (SHapley Additive Interpretation) analysis to improve transparency by identifying the most influential features in attribution of responsibility, and provide an in-depth analysis of key features and how they combine to significantly influence attribution of responsibility. Experiments conducted on real-world datasets demonstrate that AMFormer outperforms both other deep learning models and traditional approaches, achieving an accuracy of 93.46% and an F1-Score of 93%. This framework not only enhances the credibility of traffic accident responsibility attribution but also establishes a foundation for future research into autonomous vehicle responsibility.

No-fault regimes, a formerly popular alternative to the tort compensation system for auto-accident victims, have gradually lost support. Over time, premiums and claim costs have grown in no-fault states relative to other states, primarily driven by explosive medical cost increases. No-fault and tort states have also converged across many domains affecting costs, including excess claiming, litigation patterns, and noneconomic-damage payments.

The idea that humans could, at some point, develop machines that actually \"think\" for themselves and act autonomously has been embedded in their literature and culture since the beginning of civilization. But these ideas were generally thought to be religious expressions -- what one scholar describes as an effort to forge their own Gods -- or pure science fiction. One vision was uncritically Utopian. Intelligent machines, this account goes, would transform and enlighten society by performing the mundane, mind-numbing work that keeps humans from pursuing higher intellectual, spiritual, and artistic callings. The introduction of highly sophisticated autonomous machines may be literally around the corner. How the law chooses to treat machines without principals will be the central legal question that accompanies the introduction of truly autonomous machines, and at some point, the law will need to have an answer to that question.

The use of statistical and AI methods in civil litigation is an area likely to expand. As with many areas of social science, the data requirements are high but complex, because of the complexity of the legal process and the nature of the causal connections. This paper looks at the early stage of the process where the initial establishment of liability acts as a legal triage which affects the route through the litigation process. A simple model is used in which the training set is the assessment of the probability of liability given hypothetical scenarios in road traffic accidents. The model is augmented by additional “weight of evidence” assessments. The model, once built, is used as a decision support system for claim handlers on a routine basis. The methods can be seen as a way of utilising a special type of expert judgment elicitation.

Pedestrian fatality rates in the United States are markedly high compared to peer nations and are on the rise. The distribution of these deaths shows an alarming racial gap: Black pedestrians are twice as likely to be killed compared to white pedestrians. One significant factor that explains the disparity is the greater presence of wide, high-speed roads--built to move traffic quickly at the expense of pedestrian safety--in Black neighborhoods. In some cases, there is evidence that governments intentionally placed roads through and around Black neighborhoods for racially discriminatory reasons.

The criminal law on serious traffic offenses presents legislators with numerous controversial issues. One such issue is when severe consequences are matched with low moral culpability. How should the law deal with a driver who kills someone because she failed to see the person when looking? Another controversial issue concerns highly culpable behavior that remains without serious consequences. How should the law cope with a driver who nearly kills someone when overtaking recklessly? The traffic context generates many hard cases that call the outermost boundaries of general doctrinal concepts like intent, negligence, or causation into question. This book contains an international collection of essays on criminal liability for serious traffic offenses. With a focus on England/Wales, the Netherlands, France, Germany, and Spain, the book reveals that there are enormous differences in both drafting and interpretation of serious traffic offenses. Additionally, it elaborates on the role of culpability and harm in sentencing, traffic-psychological insights relevant to accident causation, and the concept of conditional intent in relation to extremely dangerous traffic behavior. (Series: Governance & Recht - Vol. 11) [Subject: Criminal Law, Traffic Law, Comparative Law]

This Article proposes an architecture of concepts and language for use in a state statute that establishes when a human occupant of an automated vehicle (AV) has contributory negligence for her interactions with a driving automation system. Existing law provides an insufficient basis for addressing the question of liability because a driving automation system intentionally places some burden for safe operation of an AV on a human driver. Without further statutory guidance, leaving resolution to the courts will likely significantly delay legal certainty by creating inefficient and potentially inconsistent results across jurisdictions because of the technological complexity of the area. To provide legal certainty, the approach recommended uses four operational modes: testing, autonomous, supervisory, and conventional. Transition rules for transfer of responsibility from machine to human clarify at what times a computer driver or human driver has primary responsibility for avoiding or mitigating harm. Importantly, specifying clear parameters for a finding of contributory negligence prevents the complexity of machine/human interactions from creating an overbroad liability shield. Such a shield could deprive deserving plaintiffs of appropriate recoveries when a computer driver exhibits behavior that would be negligent if a human driver were to drive in a similar manner.

The widespread acceptance of electric scooters has transformed the landscape of urban transportation. Yet, the emerging phenomenon of intoxicated scootering poses unanswered questions of liability and accountability. New research indicates that a third of traumatic electric scooter injuries are associated with intoxicated scootering. This statistic is particularly alarming given that there are over fifty million scooter trips per year in the United States.

There has been a dearth of longitudinal attention to the prosecution of 'road traffic deaths' in Australia and worldwide, surprising given more than 50 million people have died or been killed to date. Globally, the 'road toll' is estimated at 1.35 million per year. Almost all of those deaths are attributable to some form of human error.

Car crashes killed 32,719 Americans in 2013, and injured over 2.3 million more. Traffic is likely the most pervasive form of violence most Americans encounter. Accordingly, the law devotes substantial attention to preventing that bloodshed, allocating losses, and punishing dangerous drivers. After a serious crash, two systems of law play particularly important roles: tort law and criminal law. Both provide a mechanism for sanctioning dangerous drivers and deterring future crashes. Both can apply to the same event-any given crash is potentially criminal, tortious, both, or neither. However, tort and criminal law impose different sanctions according to different standards. After a deadly crash, for example, prosecutors may bring criminal charges under general criminal laws, like criminally negligent homicide, or traffic-specific charges, such as leaving the scene of a crash. Separately, as with any accident, victims may sue in tort for negligence. Legal scholars have long understood tort and criminal law as parallel mechanisms for sanctioning private behavior. Most have also sought to keep them separate.